Dr Lynette Pretorius is an award-winning educator and researcher specialising in doctoral education, academic identity, student wellbeing, AI literacy, research skills, and research methodologies.
Dr Basil Cahusac de Caux is an Assistant Professor with a specialisation in the sociology of higher education, postgraduate research, and the sociology of language.
In an era where generative artificial intelligence (AI) permeates every aspect of our lives, AI literacy in higher education has never been more crucial. In our recent paper, we delve into our own journeys of developing AI literacy, showcasing how educators can seamlessly integrate AI into their teaching practices. Our goal is to cultivate a new generation of AI-literate educators and graduates. Through our experiences, we also created a comprehensive framework for AI literacy, highlighting the transformative potential of embracing AI in educational settings.
We embraced AI with optimism and enthusiasm, seeing it as a tool to be harnessed rather than feared. In our recent paper, we passionately argue that AI literacy is an indispensable skill for today’s graduates. We emphasise that this mindset requires a significant cultural shift in higher education, advocating for the integration of AI as a valuable learning aid. By fostering this change, we can unlock AI’s potential to enhance education and empower students to thrive in an increasingly digital world.
Our journey began with curiosity and a willingness to experiment with AI in our educational practices. Lynette, for instance, integrated AI into her role, showcasing its capacity as an academic language and literacy tutor. She encouraged her students, many of whom are from non-English speaking backgrounds, to use tools like Grammarly and ChatGPT to improve their academic writing. By doing so, she highlighted the importance of collaboration between students and AI, promoting deeper learning and engagement.
In a Master’s level course on autoethnography, Lynette inspired her students to harness generative AI for creative data generation. She showcased how tools like DALL-E could be used to create artworks that visually represent their research experiences. This approach not only ignited students’ creativity but also deepened their engagement with their assignments, allowing them to explore their research from a unique and innovative perspective.
Basil introduced his students to the power of generative AI through hands-on assignments. One notable task involved creating a public awareness campaign centred around the UN’s Sustainable Development Goals. Students utilised DALL-E to produce compelling visuals, showcasing AI’s ability to amplify creativity and enhance learning outcomes. This practical approach not only highlighted the transformative potential of AI but also encouraged students to engage deeply with important global issues through innovative and impactful media.
While the benefits of AI in education were clear to us, we also encountered ethical considerations and challenges. In our paper, we emphasised the importance of transparency and informed consent when using AI in research and teaching. For example, we ensured that students and research participants were aware of how their data would be used and the potential biases inherent in AI-generated content. Moreover, we highlighted the environmental impact of using AI technologies. The energy consumption of AI models is significant, raising concerns about their sustainability. This awareness is crucial as educators and institutions navigate the integration of AI into their practices.
From our experiences and reflections, we developed a groundbreaking AI literacy framework for higher education, encompassing five domains: foundational, conceptual, social, ethical, and emotional. As illustrated in the figure below, this comprehensive framework is designed to empower educators and students with the essential skills to adeptly navigate the intricate AI landscape in education. By promoting a holistic and responsible approach to AI literacy, our framework aims to revolutionise the integration of AI in academia, fostering a new generation of informed and conscientious AI users.
Elements of AI Literacy in Higher Education. Download here.
From these essential domains of AI literacy, we have crafted a comprehensive framework for AI literacy in higher education.
The framework underscores the following key features:
Foundational Understanding: Mastering the basics of accessing and using AI platforms.
Information Management: Skillfully locating, organising, evaluating, using, and repurposing information.
Interactive Communication: Engaging with AI platforms as interlocutors to create meaningful discourse.
Ethical Citizenship: Conducting oneself ethically as a digital citizen.
Socio-Emotional Awareness: Incorporating socio-emotional intelligence in AI interactions.
The AI Literacy Framework for Higher Education. Download here.
Our AI literacy framework has significant implications for higher education. It provides a structured approach for integrating AI into teaching and research, emphasising the importance of ethical considerations and emotional awareness. By fostering AI literacy, educators can prepare students for a future where AI plays a central role in various professional fields.
Embracing AI literacy in higher education is not just about integrating new technologies; it’s about preparing students for a rapidly changing world. Our AI literacy framework offers a comprehensive guide for educators to navigate this transition, promoting ethical, effective, and emotionally aware use of AI. As we move forward, fostering AI literacy will be crucial in shaping the future of education and empowering the next generation of learners.
Questions to ponder
How can educators ensure that all students, regardless of their technological proficiency, can access and utilise generative AI tools effectively?
In what ways can generative AI tools be used to enhance students’ conceptual understanding of course materials?
How can the concept of generative AI as a collaborator be integrated into classroom discussions and activities?
How can educators model ethical behaviour and digital citizenship when using generative AI tools in their teaching?
How can understanding the emotional impacts of generative AI interactions improve the overall learning experience?
How can the AI literacy framework be practically integrated into different academic disciplines and curricula?
Dr Lynette Pretorius is an award-winning educator and researcher specialising in doctoral education, academic identity, student wellbeing, AI literacy, research skills, and research methodologies.
I have recently developed and delivered a masterclass about how you can develop your AI literacy in your writing and research practice. This included a series of examples from my own experiences. I thought I’d provide a summary of this masterclass in a blog post so that everyone can benefit from my experiences.
Artificial intelligence (AI) has been present in society for several years and refers to technologies which can perform tasks that used to require human intelligence. This includes, for example, computer grammar-checking software, autocomplete or autocorrect functions on our mobile phone keyboards, or navigation applications which can direct a person to a particular place. Recently, however, there has been a significant advancement in AI research with the development of generative AI technologies. Generative AI refers to technologies which can perform tasks that require creativity. In other words, these generative AI technologies use computer-based networks to create new content based on what they have previously learnt. These types of artistic creations have previously been thought to be the domain of only human intelligence and, consequently, the introduction of generative AI has been hailed as a “game-changer” for society.
I am using generative AI in all sorts of ways. The AIs I use most frequently include Google’s built-in generative AI in email, chat, Google Docs etc. which learns from your writing to suggest likely responses. I also use Grammarly Pro to help me identify errors in my students’ writing, allowing me more time to give constructive feedback about their writing, rather than trying to find examples. This is super time-saving, particularly given how many student emails I get and the number of assignments and thesis chapters I read! I also frequently use a customised version of Chat GPT 4, which I trained to do things the way I would like them to be done. This includes responding in a specific tone and style, reporting information in specific ways, and doing qualitative data analysis. Finally, I use Leonardo AI and DALL-E to generate images, Otter AI to help me transcribe some of my research, Research Rabbit to help me locate useful literature on a topic, and AILYZE to help conduct initial thematic analysis of qualitative data.
The moral panic that was initiated at the start of 2023 with the advent of Chat GPT caused debates in higher education. Some people insisted that generative AI would encourage students to cheat, thereby posing a significant risk to academic integrity. Others, however, advocated that the use of generative AI could make education more accessible to those who are traditionally marginalised and help students in their learning. I came to believe that the ability to use generative AI would be a core skill in the future, but that AI literacy would be essential. This led me to publish a paper where I defined AI literacy as:
AI literacy is understanding “how to communicate effectively and collaboratively with generative AI technologies, as well as evaluate the trustworthiness of the results obtained”.
This prompted me to start to develop ways to teach AI literacy in my practices. I have collated some tips below.
Firstly, you should learn to become a prompt wizard! One of the best tips I can give you is to provide your generative AI with context. You should tell your AI how you would like it to do something by giving it a role (e.g., “Act as an expert on inclusive education research and explain [insert your concept here]”). This will give you much more effective results.
Secondly, as I have already alluded to above, you can train your AIs to work for you in specific ways! So be a bit brave and explore what you can do.
Thirdly, when you ask it to make changes to something (e.g., to fix your grammar, improve your writing clarity/flow), ask it to also explain why it made the changes it did. In this way, you an use the collaborative discussion you are having with your AI as a learning process to improve your skills.
The most common prompts I use in my work are listed below. The Thesis Whisperer has also shared several common prompts, which you can find here.
“Write this paragraph in less words.”
“Can you summarise this text in a more conversational tone?”
“What are five critical thinking questions about this text?”
I have previously talked about how you can use generative AI to help you design your research questions.
I have since also discovered that you can use generative AI as a data generation tool. For example, I have recently used DALL-E to create an artwork which represents my academic identity as a teacher and researcher. I have written a chapter about this process and how I used the conversation between myself and DALL-E as a data source. This chapter will be published soon (hopefully!).
Most recently, I have started using my customised Chat GPT 4 as a data analysis tool. I have a project that has a large amount of qualitative data. To help me with a first-level analysis of this large dataset, I have developed a series of 31 prompts based on theories and concepts I know I am likely to use in my research. This has allowed me to start the analysis of my data and give me direction as to areas for further exploration. I have given an example of one of the research prompts below.
In this study, capital is defined as the assets that individuals vie for, acquire, and exchange to gain or maintain power within their fields of practice. This study is particularly interested in six capitals: symbolic capital (prestige, recognition), human capital (technical knowledge and professional skills), social capital (networks or relationships), cultural capital (cultural knowledge and embodied behaviours), identity capital (formation of work identities), and psychological capital (hope, efficacy, resilience, and optimism). Using this definition, explain the capitals which have played a part in the doctoral student’s journey described in the transcript.
What I have been particularly impressed by so far is my AIs ability to detect implicit meaning in the transcripts of the interviews I conducted. I expected it to be pretty good at explaining explicit mentions of concepts, but had not anticipated it to be so good at understanding more nuanced and layered meanings. This is a project that is still in progress and I expect very interesting results.
There are some ethical considerations which should be taken into account when using generative AIs.
Privacy/confidentiality: Data submitted to some generative AIs could be used to train the generative AI further (often depending on whether you have a paid or free version). Make sure to check the privacy statements and always seek informed consent from your research participants.
Artwork: Generative AIs were trained with artwork without express consent from artists. Additionally, it is worth considering who the actual artist/author/creator of the artwork is when you use generative AI to create it. I consider both the user and the AI as collaborators working to create the artwork together.
Bias propagation: Since generative AIs are trained based on data from society, there is a risk that they may reflect biases present in the training data, perpetuating stereotypes or discrimination.
Sustainability: Recent research demonstrates that generative AI does contribute significantly to the user’s carbon footprint.
It is also important to ethically and honestly acknowledge how you have used generative AI in your work by distinguishing what work you have done and what work it has done. I have previously posted a template acknowledgement for students and researchers to use. I have recently updated the acknowledgement I use in my work and have included it below.
I acknowledge that I used a customised version of ChatGPT 4 (OpenAI, https://chat.openai.com/) during the preparation of this manuscript to help me refine my phrasing and reduce my word count. The output from ChatGPT 4 was then significantly adapted to reflect my own style and voice, as well as during the peer review process. I take full responsibility for the final content of the manuscript.
My final tip is – be brave! Go and explore what is out there and see what you can achieve! You may be surprised how much it revolutionises your practices, freeing up your brain space to do really cool and creative higher-order thinking!
Questions to ponder
How does the use of generative AI impact traditional roles and responsibilities within academia and research?
Discuss the implications of defining a ‘collaborative’ relationship between humans and generative AI in research and educational contexts. What are the potential benefits and pitfalls?
How might the reliance on generative AI for tasks like grammar checking and data analysis affect the skill development of students and researchers?
The blog post mentions generative AI’s ability to detect implicit meanings in data analysis. Can you think of specific instances or types of research where this capability would be particularly valuable or problematic?
Reflect on the potential environmental impact of using generative AI as noted in the blog. What measures can be taken to mitigate this impact while still benefiting from AI technologies in academic and research practices?
Dr Lynette Pretorius is an award-winning educator and researcher specialising in doctoral education, academic identity, student wellbeing, AI literacy, research skills, and research methodologies.
In today’s data-driven world, there is a lot of talk about making decisions based on so-called objective data. For example, schools and universities use information about the mix of students and staff to shape how they teach and run things. Information such as age, where people live, how much schooling they have had, or their income is collected to help make these so-called “informed” decisions. But here’s the problem – we sometimes forget that the people collecting these forms of data and those making these decisions have their own biases. Decisions reflect the majority view, which means that other experiences are often sidelined.
We need to understand that different parts of our backgrounds interact and affect the way we experience the world, often in very different ways. This is what is termed intersectionality. Using intersectionality as a lens helps us to recognise that we cannot look at parts of someone’s identity in isolation. We need to see the whole person and how all parts of their identity come together, influencing their experiences and the way the world sees and treats them. It is like saying, “To understand the whole story, you can’t just read one page. You need to read the entire book.”
This highlights that researchers and decision-makers need to work to improve processes for data collection and analysis to better reflect the diversity of people’s experiences. So, why is it so crucial to bring diverse perspectives into the research mix?
Firstly, past research has not done a great job of representing everyone. Surveys can often be pretty narrow, missing out on the complete picture of who participants are, which means we are not getting the full story on how to solve problems for everyone.
Secondly, by embracing diversity in research, we stand up for fairness and social justice. Imagine surveys that only see concepts in black and white, leaving out people who do not fit neatly into specific boxes. We are missing out on understanding different experiences and perspectives, which can make our research richer and more meaningful.
Finally, acknowledging a wide range of experiences helps us dig deeper into our findings, giving us a clearer view of what is really going on in our context. This approach challenges us to think beyond the usual categories and consider the complex ways people identify themselves. By doing so, we can better reflect society’s diversity and push for changes that make society more inclusive and equitable for everyone.
Despite some improvements in how we collect data in recent years, there is still a long way to go. We need to ensure that our research methods allow people to share the full spectrum of their identities, respecting the richness of their experiences. It is all about giving everyone a voice and ensuring research serves us all, not just a privileged few.
The thing is, when we are exploring human experiences, we must embrace the messiness and all the different parts of who people are. But, sadly, many times, research just clumps people into simple categories, missing out on their full stories. This approach does not just overlook the richness of their identities; it can also make them feel like their voices do not matter, with their real-life experiences either ignored or questioned.
In my recent paper, I propose a new way of collecting data about research participants. I propose that we say, “Hey, let people tell us about themselves in their own words” rather than asking specific questions that limit their responses. To do this, I argue that researchers should include a question where people can share their own diversity stories when they fill out surveys. Why? Because it does justice to their experiences and knowledge.
I have seen firsthand how rich and deep data can be when people share their stories this way, especially when this data is combined with other open-ended research questions. My paper makes the case for letting people have a say in how they are represented in research. It is about giving them the power to share their identities in their own words. The main findings from my study include:
When I asked open questions, the replies were eye-opening: I decided to ask people to tell me about themselves in their own way, without the usual checkboxes. And wow, did I get a treasure trove of responses! Some people went the traditional route, but others shared stories and parts of their identities I would never have captured with a simple tick box. This approach really highlighted how everyone has their own unique blend of experiences and backgrounds.
Self-written diversity statements are gold mines of insights: One aspect that was particularly unique in my study is that I asked people to jot down their thoughts on what makes them, well, them. I did this by asking them to write their own diversity statement. The depth of what I got back was incredible – from personal tales of grappling with ableism to rich descriptions of cultural heritage and everything in between. It is like these self-written snippets opened a window into the real lives and challenges people face, way beyond what any standard survey could capture.
Weaving stories together to highlight the tapestry of people’s lived experiences: One of the most exciting findings from my study is how I used all these different bits of info from the surveys and weaved them into what I call holistic introductory stories. Imagine taking a bit from here and a snippet from there to stitch together a complete narrative about someone. It is like getting a full-colour, 3D picture of a person rather than a flat, 2D sketch. This way, I was not just seeing bits and pieces of someone’s identities, but I was developing a better understanding of how all those bits fit together to make my participants who they are.
My findings highlight the importance of encouraging epistemic justice in our research practices. What is epistemic justice, you may ask? Epistemic justice is about fairness: it ensures that everyone’s voice and knowledge are equally respected, no matter where they come from or how they express themselves. It is about ensuring all perspectives are considered, especially those often ignored or undervalued. To really do justice to everyone’s knowledge, we have to be open to different, even incomplete ways of understanding. That is why I am using open questions and these stories to give everyone a platform to share their experiences. I believe stories are how we make sense of our world. As has been highlighted by other researchers, stories help us understand not just the surface-level stuff people share but the deeper, sometimes hidden layers of their lives.
My focus has been on getting people to write down their stories because there is power in writing. But now that this study is finished, I am thinking, why stop there? There are so many other ways to share and understand each other’s experiences. So, looking ahead, I am keen on mixing things up even more, using all sorts of creative methods to make sure everyone feels seen and heard, especially those who have been left out of the conversation for too long.
Questions to ponder
If you had to write a short diversity statement about yourself, what would you say?
How does the incorporation of self-written diversity statements and open-ended questions in surveys challenge traditional methods of data collection in qualitative research?
The paper advocates for epistemic justice through methodological innovations in order to reduce biases and inequalities in research. How does giving participants the agency to define themselves challenge or change the researcher’s role?
The research outlines a more artistic way of understanding participants through holistic introductory stories. What advantages does this creative approach offer, and what challenges might it pose in traditional research environments?
Dr Lynette Pretorius is an award-winning educator and researcher specialising in doctoral education, academic identity, student wellbeing, AI literacy, research skills, and research methodologies.
Frameworks in research play a crucial role in shaping the direction of a research project. They serve as the foundation upon which studies are built and analysed, offering a lens through which researchers can interpret their findings. However, they are also a source of confusion for researchers so, in this blog post, I explain the differences between theoretical and conceptual frameworks, why they are important in research, and how researchers can choose a framework for their study.
Theoretical and conceptual frameworks: what are the differences?
The most common confusion I see when talking to researchers about frameworks is that they do not understand the difference between a theoretical and a conceptual framework. So what are the differences?
A researcher uses a theoretical framework when they are using a pre-established theory for their study. A theoretical framework is founded upon a well-established theory, focuses on explaining phenomena using this existing theory, and is chosen based on examining which theories are commonly used in the field.
A researcher uses a conceptual framework when they select concepts from several theories and construct them into a framework for a specific study. A conceptual framework is constructed by the researcher, aims to explore relationships between different concepts, and is developed by synthesising different ideas from the field together.
It is important to note that you should choose either a theoretical or a conceptual framework – usually, one project does not have both. This is because they serve the same purpose – they provide the vision for your study. Having two competing frameworks, therefore, would confuse your study. The only time you would have two frameworks is if your project had two distinct parts that were unrelated to each other.
Why do frameworks matter in research?
So why is it important to have a framework in your study? Frameworks are important because they provide a foundation and vision for your project. They help the researcher make sense of the chaos of the data by revealing the theories or concepts that will be used to analyse and explain findings. Frameworks influence:
The research design: Theoretical and conceptual frameworks help shape the research design, including the choice of data collection and analysis methods. By setting out the key concepts and their relationships, they provide a roadmap for conducting the study.
The data analysis: They offer a lens for interpreting the data collected during the research. This is particularly crucial in qualitative data analysis, where the researcher seeks to understand complex phenomena through the perspectives of participants.
The rigour and relevance of a study: The use of a theoretical or conceptual framework enhances the rigour of a study by ensuring that its findings can be linked back to broader discussions in the field.
How research builds on existing knowledge: Grounding research in a theoretical or conceptual framework ensures that new studies build upon or challenge the existing body of knowledge.
Frameworks also reveal the beliefs and assumptions of the researcher. This necessitates that the researcher carefully articulates their framework, explaining key concepts within the framework and why they are important for the particular study. Concepts can often mean different things to different people. For example, if you mention to your friend that you just adopted a dog but do not provide further details, the image of your dog in your head could be quite different from what your friend imagines. In the same way, research concepts are often multifaceted and require careful explanation in a research project.
Identifying frameworks in the literature
Identifying frameworks in published articles is a crucial skill for researchers, enabling them to understand a study’s theoretical or conceptual underpinnings, replicate research designs, or apply similar frameworks in their work. However, this is not always easy, and sometimes frameworks are not explicitly stated. So how do you identify a framework in a published study?
Look for explicit mentions: This is the easiest strategy, provided that the framework is mentioned explicitly of course. Search for sentences such as “This study is grounded in X theory” or “We employ Y’s conceptual model as a framework.”
Consider the research questions: The nature of the research questions or hypotheses can often indicate the type of framework being used.
Trace the references: Frameworks can be identified by looking at the citations of foundational works within a published source. Checking these references can provide a deeper understanding of the framework that the authors have used. Below, I have listed some of the most common frameworks used in social sciences, particularly educational research. Searching for words such as these will help you find the framework in a study.
Bourdieu’s Theory of Practice
Foucault’s theories on power, discourse, and knowledge
Bronfenbrenner’s Ecological Systems Theory
Cultural-historical theory (Vygotsky) or Cultural-historical activity theory (Vygotsky and Leontiev)
Theories of learning (e.g., community of practice, experiential learning, transformative learning)
Choosing the right framework for your research is a critical decision that shapes your study’s direction, coherence, and trustworthiness. Whether you opt for a theoretical or conceptual framework depends on your research objectives, the nature of your study, and the existing literature in your field.
The first step in choosing a framework is to clarify your research objectives: What are you trying to discover, explore, explain, or analyse? Your research objectives will guide your choice of framework by highlighting the key concepts, theories, or models that are most relevant to addressing your research questions.
The second step is to conduct an extensive literature review to identify potential frameworks that have been applied to similar research problems. By examining how these frameworks have been used in past studies, you canidentify gaps (i.e., look for areas where existing frameworks may not fully address your research problem or where there is room for further exploration), assess applicability (i.e., determine how well the frameworks align with your research objectives and the specific context of your study), and draw inspiration (i.e., even if existing frameworks do not fit your needs exactly, they can provide a starting point for developing your own).
The third step is to assess the theoretical alignment by ensuring the framework’s underlying assumptions align with your research philosophy. This involves thinking about the philosophical assumptions (e.g., your ontology, epistemology, and axiology) that underlie different frameworks and whether they are compatible with your view of the world.
The fourth step is to consider the methodological fit of each potential framework. The framework you choose should complement your research methodology, as it will influence the design, data collection, and analysis methods you will use.
The fifth step is to explore the flexibility of the framework to your specific research context. Some frameworks may be too rigid, limiting your ability to explore the nuances of your research problem fully. Others might offer the adaptability needed to address unique aspects of your study.
Finally, the sixth step is to reflect on the potential originality and contribution of the framework. Your chosen framework should enable you to make a meaningful contribution to your field. This might involve applying an existing framework in a new context, combining frameworks innovatively, or developing a new conceptual framework based on your findings.
Remember that context matters. Just because a theory is common in your field does not mean it is appropriate for your particular context. You might have to modify it by integrating theories or concepts relevant to your context, ensuring it more accurately reflects the cultural, political, religious, or additional aspects of your environment.
Conduct a context analysis: Understand the broader and specific conditions in which your research is situated.
Engage with local knowledge: Leverage insights from local experts and contextual literature to understand the context-specific nuances.
Evaluate frameworks for context sensitivity: Assess potential frameworks for their ability to accommodate or adapt to the specificities of your research context.
Consider the adaptability of the framework: Determine if the framework can be modified to better align with contextual demands without losing its theoretical integrity.
Incorporating context into the decision-making process for selecting a research framework underscores a commitment to conducting thoughtful, rigorous, and impactful research that resonates with its intended audience and addresses the complexities of the real world.
Some final thoughts…
The adoption of a well-defined framework in research is not merely a formal requirement but a cornerstone for ensuring the rigour, coherence, and depth of your study. A framework acts as a compass, guiding researchers through the complexities of their inquiry, from formulating research questions to interpreting findings. It provides a structured lens through which data can be examined, theories tested, and new insights uncovered, ensuring that research is not conducted in a vacuum but is connected to, and informed by, the wider scholarly community. Moreover, selecting and applying a framework necessitates a deep engagement with the subject matter and the methodological approaches best suited to exploring it. This engagement fosters a more nuanced understanding of the research problem and enriches the research design, ultimately enhancing the quality and impact of the findings.
Questions to ponder
What challenges might arise from applying a framework in a context different from where it was originally developed, and how can these be addressed?
Can a study be considered rigorous without a clearly defined theoretical framework? Why or why not?
Dr Lynette Pretorius is an award-winning educator and researcher specialising in doctoral education, academic identity, student wellbeing, AI literacy, research skills, and research methodologies.
Let’s talk about one of the most complex parts of research – understanding the philosophical underpinnings of your worldview and how this shapes the way your research is done. This is called a research paradigm and is one of the areas I get the most frequent questions about from graduate research students. The popularity of my research paradigm videos also show that this is clearly a concept that is difficult to understand and hard to articulate. So, in this blog post, I demystify what a research paradigm is, explore the most common research paradigms, demonstrate how a research paradigm influences the research process, and explain why it is important to articulate your paradigm in your research.
What is a research paradigm?
A research paradigm explains what you believe reality is and how you think knowledge can be understood. The technical terms used to articulate a research paradigm are ontology, epistemology, and axiology.
Ontology refers to the nature of reality. When people see reality as objective, it is called a realist ontology. When people see reality as subjective, it is called a relativist ontology. In other words, you need to explain whether you think reality is a singular, objective entity waiting to be discovered, or if it is a construct of individual experiences and perceptions.
Epistemology is concerned with the nature of knowledge and how we come to know something. There are three common epistemologies. First, reality can be objectively known if you can measure it with the right tools. Second, reality can only be partially known because it is created in the minds of individual people. Third, reality is always changing, so it is impossible to fully comprehend at any given moment.
Axiology deals with what is valued in research. It prompts us to consider how our own perspectives and values can shape the ways our research is designed and our findings are interpreted. In other words, you need to explain whether you think research should be value-free or whether you think research is value-laden. Do you think that steps should be taken to remove the researcher’s influence from the research, or is subjectivity inevitable?
What are the most common research paradigms?
I preface this discussion by noting that these are certainly not the only research paradigms that exist. Rather, these are just the ones that are most commonly found in research.
Positivism: Positivism is grounded in the belief that reality is singular and can be objectively observed and quantified. From this viewpoint, the researcher is independent of the subject of research, and knowledge is generated through empirical observation and measurement. Positivists believe that knowledge should be derived from empirical experience and logical reasoning, so they traditionally assert that research can and should be value-free.
Post-positivism: As the name suggests, this paradigm is closely related to positivism, having the same ontology and epistemology. However, post-positivists have a different axiology, arguing that complete objectivity is unattainable and that research cannot be entirely free from values, as choices about what to study, how to study it, and how findings are presented inevitably reflect value judgements. Consequently, they take steps to minimise the researcher’s influence in the way they design their studies by being reflexive about their own biases and the potential value implications of their work.
Constructivism: Constructivists believe that reality is subjective and that this reality can only be partially known because it is constructed in the minds of individual people. Constructivism emphasises that individuals’ realities are constructed through social interactions, so they seek to understand how individuals construct their realities and how these realities are influenced by social, cultural, and historical contexts. Constructivists embrace the value-laden nature of research, emphasising reflexivity and ethical responsibility, and valuing the co-construction of knowledge with participants. This paradigm acknowledges the subjective and interpretive nature of knowledge creation, viewing it as a strength that enriches understanding and contributes to the depth and authenticity of research findings. Constructivist researchers are also often concerned with the social implications of their research, seeking not only to understand the world but also to contribute positively towards social change.
Interpretivism: Interpretivists also believe reality is subjective and that reality can only be partially known because it is constructed in the minds of individual people. Interpretivism emphasises that individuals are the experts in their own experiences and focuses on understanding the subjective meanings and interpretations that individuals attach to their experiences. While interpretivism recognises the influence of researcher and participant values, it leans more towards understanding and interpreting the values and meanings inherent within the study context. Consequently, they believe that the researcher’s role is to immerse themselves in the participants’ experiences to gain a deep, empathetic understanding of their perspectives.
Pragmatism: Pragmatism is a flexible paradigm that suggests research methods should be chosen based on what best addresses the research question. Pragmatists believe reality is constantly changing or debated, so it does not commit to a single reality or method of inquiry. This allows for a mix of qualitative and quantitative methods. Pragmatism encourages an integrative approach to values, blending both subjective and objective perspectives. It recognises the importance of researchers’ and participants’ values and experiences in shaping research processes and outcomes. Yet, it also values objective evidence and the outcomes of research practices. This balance reflects pragmatism’s broader philosophical stance that truth and value are found in the practical implications of research and how it can address real-world problems.
Critical realism: Critical realists believe that an objective reality exists, but that it is layered, consisting of different levels that are not always observable. They believe our understanding of the world is subjective because it is influenced by societal and historical contexts and power relations. Critical realism is deeply intertwined with a commitment to understanding the world as it is, while also striving to transform it for the better. Critical realists believe that researchers bring their own beliefs, biases, and values to the research process, and urge researchers to critically reflect on and disclose their values and how these might impact their research.
I also recognise the importance of acknowledging that historically, some perspectives and bodies of knowledge have been overlooked or marginalised in research. This extends to the development and recognition of research paradigms as well. As such, I encourage you to also explore other diverse paradigms, especially those that may be considered ‘non-traditional’ or originate from non-Western contexts. Embracing a broader spectrum of paradigms can enrich our research approaches and outcomes, offering a wider range of valuable insights and perspectives.
How do research paradigms influence the research process?
Understanding and choosing a research paradigm is crucial because it shapes the entire research process—from framing the research question to deciding on methods, and interpreting the results. In essence, the research paradigm not only guides the technical aspects of study design, data collection, and analysis but also influences how researchers perceive and interact with their subject matter. It shapes the ethical considerations, the relationship with participants, and the ultimate goals of the research. By understanding and consciously choosing a research paradigm, researchers ensure that their work is coherent, meaningful, and aligned with their philosophical perspectives on reality, knowledge, and values. Let’s delve into how these philosophical underpinnings can shape each stage of the research process with some examples.
Formulating research questions
The paradigm a researcher adopts fundamentally shapes the nature of the questions they ask. For instance, in positivism, research questions are often framed to test hypotheses or measure variables, seeking to establish causal relationships or correlations. Constructivist, interpretivist, and critical realist paradigms, however, encourage questions that explore the meanings, experiences, and perceptions of participants, aiming to understand the complexity of human behaviour in particular contexts. These questions are then further refined based on the focus of the paradigm, leading to studies that focus on either societal change or a deeper understanding of lived experience. Pragmatism allows for flexibility, meaning research questions can be designed to solve specific problems, often leading to mixed-methods approaches.
Choosing methodologies and methods
The choice of methodologies and methods is also deeply influenced by the researcher’s paradigmatic stance. Positivists might lean towards structured methodologies that mimic the scientific method, employing quantitative measures such as surveys or experiments. Constructivists and interpretivists prefer qualitative methods like in-depth interviews, participant observation, or thematic analysis, which allow for a deep dive into participants’ lived experiences and the meanings they ascribe to them. Pragmatists select methods based on what best answers the research question, often combining qualitative and quantitative approaches in a pragmatic, problem-solving orientation. Critical realists might use a combination of qualitative and quantitative methods to explore both the surface phenomena and the underlying social or structural mechanisms contributing to these phenomena.
Data collection, analysis, and presentation of findings
Paradigms also dictate how data are collected and analysed, influencing the interaction with participants and the interpretation of data. Positivist approaches tend to emphasise objectivity and detachment, aiming for a neutral stance that minimises the researcher’s influence on the data. Findings are usually presented as objective truths or confirmed hypotheses, using statistical analysis to support conclusions. In contrast, constructivist and interpretivist paradigms view the researcher as a key instrument in the research process, engaging in reflective practices to interpret nuanced meanings within data, acknowledging their subjective influence. Findings are usually presented as insights into the participants’ perspectives, often narratively or through rich, descriptive accounts, emphasising the subjective nature of knowledge. The pragmatist paradigm focuses on practical outcomes, guiding the selection of data collection and analysis techniques that are most likely to produce actionable insights. Results are usually presented in a way that highlights their practical implications. The critical realist paradigm involves analysing data to identify not just what is happening but why it is happening, looking for patterns that reveal the influence of hidden structures or power relations. Findings are usually presented in a way which highlights or critiques the underlying societal mechanisms leading to a particular phenomenon.
Why is it important to explain your research paradigm?
Explaining the research paradigm in research publications or theses is pivotal for several reasons, each contributing to the clarity, integrity, and impact of the research. This explanation serves not just as a methodological formality but as a fundamental component that illuminates the researcher’s philosophical stance, guiding principles, and the rationale behind methodological choices. Here’s why delineating this paradigmatic foundation is crucial:
Enhancing transparency and trustworthiness: Detailing the research paradigm enhances the transparency of the study, allowing readers to understand the foundational assumptions that underpin the research. This clarity helps peers, reviewers, and readers assess the rigour or trustworthiness of the research process and its findings because it allows for an assessment of how well the research design, methodology, and methods align with the paradigmatic assumptions.
Justifying methodological choices: As noted above, the research paradigm informs the researcher’s methodological choices. By explaining this in their publications or theses, researchers provide a rationale for their methodological decisions. This explanation helps readers understand why certain methods were chosen over others and how these choices are consistent with the researcher’s philosophical stance, enhancing the coherence and integrity of the study.
Aiding interpretation of findings: Understanding the paradigmatic perspective of a study aids in the interpretation of its findings. Different paradigms can lead to different interpretations of the same phenomenon. By stating their research paradigm, researchers help readers place the findings within the appropriate conceptual and philosophical framework, ensuring that interpretations are made with an understanding of the underlying assumptions.
Contributing to disciplinary dialogue: The explicit articulation of research paradigms contributes to ongoing disciplinary dialogue and debate about the nature of reality and knowledge construction in a field. It encourages reflexivity among researchers, prompting them to consider and articulate their own positions. This reflexivity enriches the field by fostering a diversity of perspectives and approaches, leading to a more robust and dynamic academic discourse.
Helping others build upon your study: For those looking to replicate or build upon a study, understanding its paradigmatic underpinnings is essential. It ensures that subsequent research is grounded in the same philosophical assumptions, or consciously diverges from them, maintaining a level of methodological consistency and rigour across studies.
So in summary, understanding and explaining your research paradigms in publications or theses is not merely a theoretical exercise but a practical necessity that underpins the trustworthiness, clarity, and impact of research. It serves as a bridge connecting the philosophical foundations of a study with its practical execution and interpretation, enriching both the research process and its contributions to knowledge.
To cite this article, you can use this reference: Pretorius, L. (2024). Demystifying research paradigms: Navigating ontology, epistemology, and axiology in research. The Qualitative Report, 29(10), 2698-2715. https://doi.org/10.46743/2160-3715/2024.7632
Questions to ponder
Should research be value-free or is it value-laden?
In what ways do your ontology, epistemology, and axiology shape the way you conduct research?
Consider a recent study you’ve encountered. What research paradigm does it seem to align with, and how did this influence its findings and conclusions?
Dr Lynette Pretorius is an award-winning educator and researcher specialising in doctoral education, academic identity, student wellbeing, AI literacy, research skills, and research methodologies.
Lecturers play a pivotal role in shaping the learning of their students. In a metric-focused university environment, this learning necessitates the assessment of students’ learning throughout their educational journey. Assessing assignments not only gauges the understanding of the subject matter but also evaluates the development of critical academic skills. These skills, such as research, analysis, and effective communication, are integral components of a well-rounded higher education.
Assessing transferable skills
The skills assessed must align with what is taught within the unit. When students perceive a direct connection between what is taught and what is assessed, their engagement and comprehension are heightened. Consequently, if we are going to assess students not only on their content knowledge but also their transferable skills, we need to provide them with the tools to succeed.
I believe that transferable skills enhance the applicability of students’ disciplinary knowledge. For years, I have worked to develop a suite of academic skills resources which are now embedded across the units within our Faculty. These resources include a suite of just-in-time online videos freely available on YouTube, as well as two written booklets (Doing Assignments and Writing Theses) that explicitly teach academic communication skills.
Over the years, I have also worked to improve the assignment rubrics within our Faculty to more accurately assess the skills that are taught within individual units. For example, I have worked with another staff member to develop templates for staff to provide feedback on academic language and literacy. We designed these templates to allow assessors to label specific mistakes for students and to provide students with referrals to appropriate support. Giving students specific labels for their errors helps them to see where they can improve. The referrals to appropriate resources and support help the student improve their skills, encouraging self-directed learning.
It is important to note that we usually recommend that these skills account for no more than 10% of the total grade for the assignment. This is because the main focus of the assessment should be the content – students should be able to clearly demonstrate an understanding and critical evaluation of the topic of the assignment. However, the students’ use of academic language and academic literacy can enhance the quality of their disciplinary content, or it can hinder the meaning of their ideas. As such, our templates allow for 5% to be attached to academic language (specifically, the elements listed in blue here) and 5% to academic literacy (the elements listed in purple here).
Assessing AI literacy
In the era of rapid technological advancement, the rise of generative artificial intelligence (AI) introduces a new dimension to education. As students are increasingly exposed to AI tools, it becomes imperative for educators to teach them how to use these tools effectively. As I have highlighted in another blog post, I firmly believe that it is our role as educators to teach students how to collaborate effectively with AI and evaluate the results obtained, a concept termed AI literacy. I see AI literacy as an essential transferable skill.
A key component of using AI ethically is acknowledging it effectively in written work. It is important to highlight, though, that if we are going to require students to demonstrate AI literacy, including the accurate acknowledgement of the use of AI tools, we need to teach it in our units and also assess it accurately. In my units, I teach students that an acknowledgement should include the name of the AI used, a description of how it was used (including the prompt used where appropriate), and an explanation of how the information was then adapted in the final version of the document. I also provide students with the example below so that they can see how an acknowledgement is used in practice.
I acknowledge that I used ChatGPT (OpenAI, https://chat.openai.com/) in this assignment to improve my written expression quality and generate summaries of the six articles I used in the annotated bibliography section. The summary prompt provided to the AI was “Write a 350 word abstract for this article. Include a summary of the topic of the article, the methodology used, the key findings, and the overall conclusion”. I adapted the summaries it produced to reflect my argument, style, and voice. I also adapted the summaries to better link with my topic under investigation. When I wanted the AI to help me improve my writing clarity, I pasted my written text and asked it to rewrite my work “in less words”, “in a more academic style”, or “using shorter sentences”. I also asked it to explain why it made the changes it did so that I could use this collaborative discussion as a learning process to improve my academic communication skills. I take responsibility for the final version of the text in my assignment.
Clear guidelines within rubrics should also be established to evaluate the ethical and responsible use of AI, reinforcing the importance of acknowledging the role of these tools in academic work. Given my previous work developing rubric templates for staff, I have recently developed a template for the acknowledgement of AI use within assignments. In my template, this criterion falls within the “academic literacy” section of the rubric I mentioned earlier. I have included the rubric criteria below so that other educators can use it as needed. The grading scale is the one used in my university, but it can be easily adapted to other grading scales.
High Distinction (80-100%): There was an excellent explanation about how generative AI software was used. This included, where appropriate, explicit details about the software used, the prompts provided to the AI, and explanations as to how the output of the generative AI was adapted for use within the assignment.
Distinction (70-79%): There was a clear explanation about how generative AI software was used. This included, where appropriate, sufficient detail about the software used, the prompts provided to the AI, and explanations as to how the output of the generative AI was adapted for use within the assignment.
Credit (60-69%): There was a reasonably clear explanation about how generative AI software was used. The explanation lacked sufficient details regarding one of the following: the software used, the prompts provided to the AI, and/or explanations as to how the output of the generative AI was adapted for use within the assignment.
Pass (50-59%): There was some explanation about how generative AI software was used. The explanation lacked several of the following: the software used, the prompts provided to the AI and/or explanations as to how the output of the generative AI was adapted for use within the assignment.
Fail (Below 50%): There was little or no explanation about how generative AI software was used.
Questions to ponder
The blog post outlines a rubric for assessing the acknowledgement and use of generative AI in student assignments. Considering the varying levels of detail and adaptation of AI-generated content required for different grades, what are your thoughts on the fairness and effectiveness of this approach?
How might this rubric evolve as generative AI technology becomes more advanced and commonplace in educational environments?
Dr Lynette Pretorius is an award-winning educator and researcher specialising in doctoral education, academic identity, student wellbeing, AI literacy, research skills, and research methodologies.
Autoethnography has become an increasingly popular research methodology, particularly within the humanities and social sciences. I use it regularly because of its emphasis on personal experiences, reflexivity, and storytelling which allows for a deeper exploration of complex experiences and societies. So what is autoethnography? The name autoethnography comes from three core aspects: self, culture, and writing. So, literally, autoethnography is an approach to research and writing that seeks to describe and systematically analyse personal experience to better understand cultural experiences.
As I’ve noted in a recent book chapter, there are several reasons why I find autoethnography a particularly compelling research methodology.
First, autoethnography allows researchers to purposely explore personal experiences to understand a particular culture or society. Researching personal experiences is becoming increasingly important as individuals’ stories are recognised as important sources of knowledge. Personal experiences can provide unique insights into social, cultural, and historical contexts and highlight the complexities of human experience.
Second, autoethnography considers insider knowledge as a valuable source of data. Researchers are the participants in their own studies and the stories which are told often explore transformative experiences for the researcher, frequently taking the form of epiphanies that significantly influenced the author’s worldview. I believe that this allows researchers to provide more meaningful insights into complex phenomena compared with more traditional objective research methods.
Third, autoethnography empowers researchers as it allows them to embrace emotionality and uncertainty and highlight topics that may be considered hidden or taboo. Autoethnography allows researchers to connect with their own emotions and experiences and, in doing so, find their voice. It allows them to challenge the dominant narratives that often dominate research and to tell their own stories in their own words.
And finally, autoethnography is a more accessible type of research for those outside of academia because it is written from personal experience in easy-to-understand language. The autoethnographer also does not merely narrate an experience for their audience. Instead, they try to engage the audience in the conversation so that the audience can understand experiences which may be different from their own. By sharing their own experiences, they can create a space for others to share theirs, fostering a more equitable and inclusive research process.
It is important to note that autoethnography does have some challenges. Some researchers critique it as a methodology because it is not scientific enough, while others say it is not artistic enough. I believe, however, that these critiques fail to see the value of combining both science and art when exploring complex phenomena. In this way, autoethnographers can advocate for social change to address perceived societal wrongs.
So how do you actually do autoethnography in your research project? It is important to remember that there is no one way to do autoethnography. What is most important is to develop systematic data collection and analysis methods that help you deeply explore your personal experience.
First, it is important to have a series of reflective prompts to help you explore your experiences. I use a simple prompt strategy, which gives very open initial prompts to allow me to delve into my personal experiences, analyse my emotions and thoughts during that period, reflect on how I feel now, and determine how my previous experiences have impacted my current philosophy or practice.
Describing the experience
What happened?
What did I do?
Analysing the experience
What was I thinking and feeling?
How do I feel now?
What went well?
What could I have done better?
Creating a step-by-step plan
How will this information be useful in the future?
How can I modify my practice in the future?
What help do I need?
Second, you need a way to record your reflections. I like to start my reflection journey by voice or video recording a conversation I have with myself, thinking about my past experiences. I start by thinking about what happened, what I did, what I was thinking and feeling at the time, and how I feel now. Then, I explore how I think the experience has informed my way of being now. How has it shaped my future practice? Why? After finishing the recording, I transcribe the recording and use this transcription as my initial data. I have also recently used discussions and images created with generative AI as part of my autoethnography data generation process. If you want to see how that is done, you can watch this video.
Third, you can also consult relevant artefacts as part of your autoethnography, such as photos and documents from the past to help you think and reflect more deeply about an experience. You can also consult other important figures such as family or friends from your past to help you see the experience from multiple viewpoints. It is important to note that you will require ethics approval for your study if you use photos with other people in them, or the significant people you consult are possibly identifiable in your final project.
Fourth, you use the writing process as part of your reflection process. Through the writing process, you further reflect on what you were thinking and feeling during the experiences you are describing. These reflections can remind you of other experiences that shaped your understanding of that experience. This continuous writing and re-writing of your story becomes further data sources that allow you to engage more deeply with your experiences. Remember to lean into your story’s more emotive and vulnerable parts, as this will allow you to uncover hidden perspectives in your understanding more effectively. Ask yourself, why did this experience make me feel this way? What does it tell me about the context I found myself?
Finally, as you write about your experiences, you should incorporate your theoretical analysis. Start looking for key concepts you have identified in your reflections and how they link to your overarching research problem. Which theoretical concepts do they reflect? What can others learn from your experience?
In conclusion, good quality autoethnography explores personal experiences to illuminate a particular cultural context. Autoethnography is not merely telling your story. It is analysing your story to uncover previously ignored perspectives within a particular research context.
Autoethnography emphasises the value of personal experiences in understanding cultural contexts. Reflect on an experience from your life that could offer unique insights into a particular cultural or societal aspect. How could analysing this personal experience using autoethnography enhance our understanding of broader cultural phenomena?
What are your thoughts on balancing the scientific rigour and artistic expression in autoethnography? Can you think of any specific situations or contexts where this methodology might be particularly beneficial or problematic?
Dr Lynette Pretorius is an award-winning educator and researcher specialising in doctoral education, academic identity, student wellbeing, AI literacy, research skills, and research methodologies.
Credit: This blog post is an adapted form of a recent paper I wrote.
Artificial intelligence (AI) has been present in society for several years – think, for example, of computer grammar-checking software, autocorrect on your phone, or GPS apps. Recently, however, there has been a significant advancement in AI research with the development of generative AI technologies like ChatGPT. Generative AI refers to technologies which can perform tasks that require creativity by using computer-based networks to create new content based on what they have previously learnt.
For example, generative AI technologies now exist which can write poetry or paint a picture. Indeed, I entered the title of one of my published books (Research and Teaching in a Pandemic World) into a generative AI which paints pictures (Dream by WOMBO). The response it generated accurately represented the book’s content, was eye-catching, and I believe it would have been a very suitable picture for its cover. Check it out:
(Note: This response was generated by Dream by WOMBO (WOMBO Studios, Inc., https://dream.ai/) on December 12, 2021 by entering the prompt “research and teaching in a pandemic world” into the generator and selecting a preferred style of artwork.)
The introduction of generative AI has, however, led to a certain amount of panic among educators; many workshops, discussions, policy debates, and curriculum redesign sessions have been run, particularly in the higher education context. Educators acknowledge that there is a need to accept that generative AI can also be leveraged to support student learning. In fact, it is clear that students will likely be expected to know how to use this technology when they enter the workforce. Importantly, though, there has also been significant concern that generative AI would encourage students to cheat. For example, many educators fear that students could enter their essay topic into a generative AI and that it would generate an original piece of work for them which would meet the task requirements to pass.
I believe what is missing from these discussions regarding generative AI is the fact that assessment regimes focus predominantly on the product of learning. This focus assumes that the final assignment is indicative of all the student’s learning but neglects the importance of the learning process. This is where generative AI can be a valuable tool. From this perspective, the technology should be considered as an aide, with the intellectual work of the user lying in the choice of an appropriate prompt, the assessment of the suitability of the output, and subsequent modification of that prompt if the output does not seem suitable. Some examples of the use of generative AIs as an aide include helping students develop an outline or brainstorm ideas for an assignment, providing feedback to students on their work, guiding students in learning how to improve the communication of their ideas, and acting as an after-hours tutor or a way for English-language learners to improve their written skills. Using generative AI in this more educative manner can help students better engage with the process of their learning.
In a similar way to when Microsoft Word first introduced a spell-checker, I believe generative AI will become part of our everyday interactions in a more digitally connected and inclusive world. Importantly, though, as mentioned above, while generative AI may help the user create something, it is dependent on the user providing it with appropriate prompts to be effective. The user is also responsible for evaluating the accuracy or usefulness of what is generated. As such, we need to teach students how to communicate effectively and collaboratively with generative AI technologies, as well as evaluate the trustworthiness of the results obtained – a concept termed AI literacy. I believe AI literacy is likely to soon become a key graduate attribute for all students as we move into a more digital world which integrates human and non-human actions to perform complex tasks.
In my teaching practice, I now advise students to use generative AI as a tool to help them improve their approaches to their assignments. I suggest, in particular, that generative AI can be used as a tool to start brainstorming and planning for their assignment or research project. I include examples of how generative AI can be used for various purposes in my classes. For example, I highlight that generative AI may be able to assist a researcher in generating some starting research questions, but it is the researcher’s responsibility to refine these questions to reflect their particular research focus, theoretical lens, and so on. I emphasise to students that generative AI will not do all the work for them; they need to understand that they are still responsible for deciding what to do with the information, linking the ideas together, and showing deeper creativity and problem-solving in the final version of their work.
I have recently showcased this approach in a video which is freely available on YouTube. The first video (Using generative artificial intelligence in your assignments and research) explains what generative AI is and what it can be used for in assignments and research. The second video (Using generative AI to develop your research questions) showcases a worked example of how I collaborated with a generative AI to formulate research questions for a PhD project. These videos can be reused by other educators as needed.
This video starts by showing students how I have used ChatGPT to brainstorm a starting point for a research project by asking it to “Act as a researcher” and list the key concerns of doctoral training programmes. In this way, I show the students the importance of prompt design in the way they collaborate with the generative AI. In the video, I show that ChatGPT provided me with a list of seven core concerns and note that, using my expertise in the field, I have evaluated these concerns and can confirm that they are representative of the thinking in the discipline. In the rest of the video, I showcase how I can continue my conversation with the generative AI by asking it to formulate a research question that investigates the identified core concerns. I show students how I collaborated with the generative AI to refine the research question until, in the end, a good quality question is developed which incorporates the specificity and theoretical positioning necessary for a PhD-level research question.
It is important to note that students are likely not yet experts in their field when they are designing their research questions. Therefore, it is important to provide them with guidance as to how to evaluate the ideas produced by generative AI. This includes highlighting that a generative AI is not always accurate, that it may disregard some information which may be pertinent to a specific research project, or that it may fabricate information. Students need to learn that a generative AI is not a tool similar to an encyclopedia which contains all the correct information. Rather, generative AI is a tool which responds to prompts by generating answers it “thinks” would be appropriate in that particular context. Consequently, I advise students to use generative AI as a starting point, but that they should then explore the literature to further assess the accuracy of the core concerns identified earlier as well as the viability of the research question for their project.
It is also worth noting that generative AI could be used as a way to help students see what a good research question might look like, rather than using it specifically to develop a research question for their particular research project. Generative AI may also be useful in helping students see how to organise the themes in the literature. In this way, we encourage students to use generative AI as part of the learning process, allowing them to scaffold their skills so that they can use their creativity and other higher-order thinking skills to further advance knowledge in their discipline.
Students should also be taught how to appropriately acknowledge the use of generative AI in their work. Monash University has provided template statements for students to use. I use these template statements as part of my regular workshops. In this way, I show students that ethical practice is to acknowledge which parts of the work the generative AI did and which parts of the work were done by a person.
I have also recently used such an acknowledgement in one of my research papers. I have included it below for other researchers to use in their work.
I acknowledge that I used ChatGPT (OpenAI, https://chat.openai.com/) to generate an initial draft outline of the introduction of this manuscript. The prompt provided for this outline was “Act as a social science researcher and write an outline for a paper advocating for change to survey design to collect more diverse participant information”. I adapted the outline it produced for the introduction to reflect my own argument, style, and voice. This section was also significantly adapted through the peer review process. As such, the final version of the manuscript does not include any unmodified content generated by ChatGPT.
As with all new technologies, there are potential challenges and risks that should be considered. Firstly, generative AI technologies can generate results which seem correct but are factually inaccurate or entirely made up. Secondly, there is the issue of equity of access. It is incumbent upon us as educators to ensure that all students have equal access to the technologies they may be required to use in the classroom. Thirdly, there is the risk that the generative AI may learn and reproduce biases present in society. Finally, for researchers, there are also ethical concerns relating to the retention and possible generation of potentially sensitive data.
Generative AI is, at its core, a natural evolution of the technology we already use in our daily practices. In an ever-increasingly digital world, generative AI will become integral to how we function as a society. It is, therefore, incumbent upon us as educators to teach our students how to use the technology effectively, develop AI literacy, and use their higher-order thinking and creativity to further refine the responses they obtain. I believe that this form of explicit modelling is how we, as educators, can help students develop an understanding of generative AI as a tool to improve their work. In this way, we focus on the process of learning, rather than being so focused on the ultimate product for assessment.
Questions to ponder
How do you think AI literacy can be integrated into current educational curricula to enhance learning while ensuring academic integrity? What are the potential challenges and benefits of incorporating generative AI into classroom settings?
How should students and researchers navigate the ethical implications of using AI-generated content in their assignments and research?
Dr Lynette Pretorius is an award-winning educator and researcher specialising in doctoral education, academic identity, student wellbeing, AI literacy, research skills, and research methodologies.
Credit: This blog post is an adapted form of a case study I wrote for Advance HE.
Universities have been cracking down on cheating and all sorts of dishonest academic behaviour recently. They’ve rolled out a bunch of strict rules related to academic integrity and use fancy software to keep an eye out for academic misconduct. In this space, there’s this idea floating around that you should either focus your attention entirely on fighting cheating or you should only be championing academic honesty (Dawson, 2021). However, I believe that this is a false dichotomy. It’s not just about telling students what not to do, even though this is of course important; it’s also about getting them involved in the process, making them understand and own up to their responsibilities. It’s teaching them the ropes of being academically honest through real experiences. In this way, we create a culture of academic integrity (Cutri et al., 2021). This means encouraging students to think about their own academic integrity practices, talking about academic integrity openly, and using mistakes as teachable moments, especially when it comes to plagiarism.
Encouraging students to think about their own academic integrity practices
I’m a big believer in the power of self-reflection because I know that reflecting on your own experiences and beliefs can really open your eyes, spark growth, and sharpen your skills (Cahusac de Caux et al., 2017). That’s why I always make sure my students get the chance to think about their own academic integrity practices. For example, I recently completed a project with some PhD students where we dived into the research on academic integrity and they got to reflect on why they approached academic integrity in certain ways. It was eye-opening for them to see how their academic identities shaped their approaches to academic integrity. One student, for example, mentioned how coming from a country where textbooks were almost worshipped, they found it difficult to critically analyse other studies. They weren’t used to pointing out flaws or gaps in research, which led them to rely a lot on direct quotes. This project showed us that sometimes it’s a lack of confidence that drives how students write. We ended up developing a model of academic integrity at the doctoral level, which highlighted how feeling like an impostor can lead to plagiarism and other dishonest academic practices. We published our findings in an open-access paper in 2021 and you can access it by clicking on the button below.
Over the last decade, I’ve been developing different ways to help students get better at playing by the academic rules, including workshops, online videos, and something I call the Practice Turnitin Assignment. Every semester, I run a workshop named “Referencing and Academic Integrity”. It’s open to all the students in my faculty, and it’s all about understanding what counts as plagiarism, how to make sure work is original, and what is considered the right way to reference sources in our faculty. All the notes for this workshop are provided beforehand and are also publicly available through our Doing Assignments Booklet. If you would like to use these notes, you can download them by clicking on the button below.
I’ve been teaming up with my colleagues to improve how we describe assignments, design our marking guides, and give feedback. We’ve been making a real point of showing how crucial it is to back up arguments with solid evidence. It’s all about emphasising the importance of being honest in your work. This includes explicit marking rubric criteria linked to the use of references to support work as well as clear criteria associated with formatting the references correctly. This is because these two things are separate academic skills – one focuses on being able to support your arguments while the other emphasises being able to follow a template.
I also decided to develop a bunch of snappy videos in YouTube. I’ve played around with different styles for these videos, and students can pick and choose what they watch and in what order. It’s great for giving students the info they need, right when they need it. You can learn more about how I designed these videos here. Turns out, my YouTube channel’s a bit of a hit – it’s racked up over a million views last time I checked! Over 8,000 people have also subscribed so that they can be notified when I create new videos. The best bit? I’ve made all my videos freely available, so any educator out there can use them in their classes. Check out my channel by clicking on the button below.
Finally, I developed a resource called the “Practice Turnitin Assignment” which is available to all students in my faculty. My university uses Turnitin to spot any copied work, but I figured why not use it as a teaching tool as well? I set up a special Turnitin assignment where students can submit their work, but no staff checks it and it doesn’t get stored in Turnitin’s database. This means students can use the Practice Turnitin Assignment to test their summarising and paraphrasing skills, see where they might be going wrong, and fix their work before they submit it. In this way, I am encouraging students to check the academic integrity of their work as part of their assignment writing process.
Using mistakes as teachable moments
Let’s be real, though. Even with all my hard work, my colleagues and I will still spot cases of plagiarism every semester. Most of the time, it’s not because students are trying to purposely cheat. More often than not they just don’t understand how to apply the rules (e.g., they just don’t know how to reference properly). Sure, this does necessitate penalties, like failing the assignment, but I also see this as a chance for a teachable moment.
That’s why I’ve set up a process where, when a student slips up and it’s clear they didn’t mean to, their lecturer can send them my way. I sit down with them and we go over how they can improve their academic integrity practices in the future. After our discussion, I give them a special hurdle task that is all about taking a piece of text and rewriting it in their own words, in just one paragraph. This way, they get to apply the skills we talked about, demonstrating that they’re ready to apply improved academic integrity skills in their future assignments. If this sounds like something you would like to use yourself, you can download the task below.
Have you ever had a moment of realisation about your own academic integrity practices? How did this awareness influence your approach to academic work, and what steps did you take to enhance your understanding and application of academic integrity principles?
In your opinion, what is the right balance between using technology to prevent cheating and educating students about academic integrity?
Dr Lynette Pretorius is an award-winning educator and researcher specialising in doctoral education, academic identity, student wellbeing, AI literacy, research skills, and research methodologies.
For many years now, I have been working to improve the experiences of PhD students. One practice I’ve found particularly useful is incorporating collaborative and peer-based learning through doctoral writing groups. My work with writing groups started way back in 2013 and, over more than a decade, I have further refined my approach. I currently facilitate four such groups on a fortnightly basis. Writing groups embody some of the most important aspects of learning: working together to co-construct personal knowledge through experience, constantly reflecting on one’s own understanding to improve professional practice, and building rich experiences that inspire learning and foster an environment of empowerment.
My approach to teaching in these groups is unique: doctoral writing groups are not common and, even in settings where they are available, they are usually run in a very different manner. My doctoral writing groups are set up as a peer-based environment where small groups of students receive feedback on their academic writing from the facilitator and their fellow students. There are three sections of each writing group meeting:
Collegial chat: Meetings start with a friendly discussion time where participants can share their doctoral journeys over the past two weeks.
Reflection: Ten minutes of discussion where students who shared their written work in the previous meeting reflect on how they have incorporated the feedback they received.
Feedback and discussion: The rest of the meeting is focused on students sharing their written work and receiving feedback on areas for improvement in a peer-learning environment.
My writing groups have been set up in this way to create a space for authentic learning about actual writing, where peers support peers. Participants discuss suggestions for improvement as a group, fostering an environment where all participants learn from the feedback provided. As such, in many ways, the learning in a doctoral writing group is a continuous process of reading, discussion, personal reflection, and peer-based learning. In this way, the writing group becomes a site of academic social practice.
I also wanted to create a collegial space in which any question would be valid at any stage of the process. To achieve these goals, modelling of the academic writing process was particularly important. During meetings, I will regularly share draft documents I am currently writing, explaining to the writing group what I aim to achieve with that text. I will then also model how I would provide feedback to myself, highlighting errors in logic, poor phrasing, lack of evidence, or other academic language and literacy issues. Through this modelling, students gain an authentic insight into how academic writing is actually done. This helps to normalise the concept of writing as a process and helps them to learn how to critique others’ (and their own) work.
Collegiality is the cornerstone of the success of this type of group. Feedback discussions and personal reflections would not be effective if the students do not feel safe and part of the learning community. It is important to create a safe space to allow for the collegial critique of each other’s written work. I do this by establishing expectations from the beginning. Each participant is provided with the writing group’s code of conduct. If you want to create a code of conduct for your writing group, you can use the one below.
Ensuring a safe space
In order to ensure that all participants are treated with respect, we should behave in a manner that affirms the worth, dignity, and significance of all participants.
As part of the writing group, you are supposed to critique other’s work, but this should never be done in a way that disrespects the other person. Do not use language that devalues another person or the significance of their research. All participants in the writing group have the same right to be there and should be treated in a way that affirms their worth and significance.
Be respectful with the words you use when you talk to or about others. Listen to others and take note of others’ reactions to your tone of voice and manner.
Never use derogatory language, put downs, racist or sexist language, even sarcastically or as a joke.
Show respect for other cultures, traditions, or religions. Remember that everyone does not necessarily think the way you do. Avoid statements that reflect ignorance or bias about other cultures, traditions, or religions.
Have a zero tolerance for discrimination. If you believe someone is behaving in a discriminatory way, you should feel comfortable to raise the issue in the group or by talking to Lynette afterwards. We do not condone any discriminatory behaviour in the writing group setting.
Respectfully critique someone else’s work
When giving feedback to another participant, start by highlighting what you thought was done well in the text you read.
Focus on areas for improvement in academic style and language. This can include suggestions for improvement in referencing, style, voice, organisation of ideas, as well as any area of English language.
If you are knowledgeable about the topic that the other person wrote about in their text, you can also provide them with suggestions for improvement in content. This can include suggestions for further readings, as well as theories or concepts that can be added to strengthen the arguments in the text.
Want to learn more about the benefits of academic writing groups? My research has demonstrated that writing groups are spaces for academic pastoral care which foster academic identity and sense of belonging. You can learn more by watching the research presentation or reading the paper below. Why not start a writing group today?
Have you ever participated in a doctoral writing group or a similar peer-based learning environment? Share your experiences regarding how this setup impacted your learning, writing skills, and academic identity. Did you encounter any challenges in giving or receiving feedback, and how did you overcome them?
In your opinion, what are the key elements of effective feedback in an academic setting? How can such feedback contribute not only to the improvement of academic writing but also to the development of a sense of belonging and academic identity among doctoral students?
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