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‘A demure approach to GenAI’: An autoethnography on Generative AI’s transformative impact in higher education

Full paper

Published onJan 27, 2025
‘A demure approach to GenAI’: An autoethnography on Generative AI’s transformative impact in higher education
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Abstract

In an era where technology is reshaping the educational landscape, this paper ventures into the heart of innovative pedagogy. This study comprehensively explores how Generative AI (GenAI) tools can assist educators with designing and planning educative materials and support educational research from a first-hand inquiry. Through an autoethnographic approach, this study reveals that GenAI fosters creativity, efficiency and innovation in teaching preparation and practices, highlighting opportunities to enhance the efficiency and effectiveness of educators’ practices inside and outside the classroom setting. These findings were validated through member-checking interviews, where participants affirmed the benefits of GenAI in fostering teaching strategies and streamlining educational workflows. However, the autoethnographic narratives and member-checking interviews identified significant challenges to integrating GenAI. These included a steep learning curve, risks of overreliance, and ethical concerns such as privacy and academic integrity. While the researcher demonstrated a drive to champion GenAI, member-checking participants expressed ongoing scepticism, citing limited training and a lack of policies as barriers to full adoption. This resistance underscores the importance of addressing these gaps to support the successful integration of AI in education. The study concludes with recommendations for policymakers to prioritise educator training, establish clear integration policies, and promote collaborative shared practices. By intertwining reflective practice with the Technological Pedagogical Content Knowledge (TPACK) framework, this research offers practical insights into leveraging GenAI to advance pedagogy. Ultimately, it empowers educators to navigate digital transformation confidently, balancing innovation with thoughtful, ethical implementation to enrich educational outcomes.

Keywords: Educators; generative AI; autoethnography; ChatGPT; experiences; TPACK

Part of the Special Issue Generative AI and education

1. Introduction

1.1 Background and rationale

In the rapidly evolving landscape of education, technological advancements, including artificial intelligence (AI), play a pivotal role in transforming traditional teaching and learning practices (Baidoo-Anu & Ansah, 2023). Generative AI (GenAI) stands out as a revolutionary force with the potential to significantly enhance educational experiences and outcomes (Chiu, 2024). GenAI refers to technology that uses deep learning models to create human-like content, such as images and text, based on complex and diverse prompts (Michel-Villarreal et al., 2023). This recent advancement has created new opportunities in higher education by generating sophisticated and authentic content for personalised learning (Chiu, 2024). Examples of GenAI applications used in higher education include chatbots, intelligent tutors and text generation, offering innovative ways to streamline lesson preparation, enrich teaching methodologies, and facilitate innovative classroom activities (Zhang & Aslan, 2021). While extensive research discusses the benefits (Alwaqdani, 2024; Baidoo-Anu & Ansah, 2023) and uses (Farrelly & Baker, 2023; Su & Yang, 2023) of GenAI in educational settings, there is a lack of self-reflective studies illuminating the experiences of educators using GenAI with real-life applications. To address this gap, autoethnography was adopted to offer a deeper understanding of the emotions involved in using GenAI. This qualitative research method uses autobiographical writing to draw upon experiences as a complete member researcher (Anderson, 2006). Such a research design enfolds a deeper understanding of emotions experienced (Hammarberg et al., 2016) from a first-hand encounter. However, as an emerging genre, autoethnography has faced criticism (Holt, 2003), with some authors labelling autoethnography ‘too self-indulgent’, ‘narcissistic’ (Coffey, 1999) and ‘at the boundaries of academic research’ (Sparkes, 2000). Despite these critiques, autoethnography is distinguished by its emphasis on sharing personal narratives that convey emotional experiences through storytelling (Chang, 2016). This approach engages readers in a constructive interpretation of the researcher’s perspectives and distinguishes it from other qualitative approaches.

The study reported in this paper aimed to provide a rich, contextual understanding of the integration of GenAI tools into educators’ practices through personal narrative studies. By utilising a qualitative, autoethnographic approach, this study delved into the demure yet profound personal experiences, reflections, and the practical implications of using GenAI in a higher education setting.

The insights gained from this exploration are expected to contribute valuable knowledge to the education field, highlighting the real-life struggles and opportunities associated with GenAI integration. The study was guided by the following research questions:

  • RQ1: What are the experiences of an educator integrating GenAI tools into educational practices?

  • RQ2: How can GenAI tools enhance the efficiency and effectiveness of the educators’ practices?

  • RQ3: What are the perceived struggles and opportunities of using GenAI tools from the educators’ perspectives?

1.2 Significance of the study

This study holds significant relevance in the contemporary educational context, where GenAI is increasingly becoming an integral part of the teaching and learning ecosystem (Su & Yang, 2023). By documenting and analysing the firsthand experiences of the researcher, this research provides practical examples and reflections that can inform and empower other educators, policymakers, and researchers alike. The findings are anticipated to shed light on the potential of GenAI for educational practices, addressing key aspects such as efficiency, effectiveness, and the nuanced challenges that may arise, hoping to inspire other educators to take the leap towards implementing GenAI.

2. Literature review and theoretical approach

2.1 The integration of GenAI in education

GenAI tools are increasingly being applied in education to push the boundaries of teaching and learning processes. Their integration has been shown to foster pedagogical innovation, particularly in lesson planning, content generation and academic research (Rahman & Watanobe, 2023). Studies demonstrate that AI-driven assessment automation reduces the time educators spend on grading, enabling them to focus on more critical aspects of teaching (Alwaqdani, 2024). In terms of content creation, tools like ChatGPT (Generative Pre-trained Transformer) assist in lesson planning by generating materials tailored to specific learning objectives (Michel-Villarreal et al., 2023). Baidoo-Anu and Ansah (2023) further argue that GenAI significantly enhances teachers’ time management and productivity, allowing them more student engagement. GenAI also fosters creative thinking in teaching, supporting active learning strategies that encourage student participation (Su & Yang, 2023).

Moreover, GenAI enables personalised learning by adapting content to meet individual student needs (Chiu, 2024), creating tailored instructional approaches that have been shown to level the playing field for students with disabilities (Farrelly & Baker, 2023). This demonstrates how AI tools can complement pedagogical knowledge to create dynamic learning environments.

2.2 Personalised learning and inclusivity

Despite its potential, researchers caution users to approach GenAI with an awareness of its limitations and risks. Rachman and Watanobe (2023) highlight ethical concerns, including bias, discrimination, privacy, security, misuse, accountability, transparency, and broader social impact. These challenges contribute to educators’ hesitation to fully embrace the technology. Moreover, Alwaqdani (2024) found that while educators recognise the potential of AI tools, they also express concerns about the effort required to learn and adapt to these technologies. Effective integration, the study argues, necessitates robust training and ongoing support to align AI tools with pedagogical strategies.

2.3 The role of TPACK in understanding GenAI adoption

The Technological Pedagogical Content Knowledge (TPACK) framework provides an effective lens for understanding these complexities. As Mishra et al. (2023) highlight, TPACK bridges the gap between theoretical insights and practical application by emphasising the interplay of technology, pedagogy, and content knowledge. This framework supports a comprehensive understanding of how technologies can be utilised to enhance educational outcomes (Koehler & Mishra, 2009).

Studies applying TPACK to GenAI in education have demonstrated the framework’s relevance. Mishra et al. (2023) explored how TPACK provides a lens to understand how GenAI will reshape individuals, society, and the broader educational context, influencing teachers’ knowledge and practice. Cun and Huang (2024) applied TPACK to examine pre-service teachers’ perspectives on using generative AI tools, specifically ChatGPT, for teaching and learning. Yang et al. (2025) utilised the TPACK framework to investigate teachers’ willingness to integrate GenAI in educational settings. Their study identified challenges, such as how negative emotions can weaken TPACK’s effect. The authors emphasised the importance of emotional and psychological support to mitigate these negative emotions, underscoring the need for tailored strategies to support teachers during adoption.

2.4 Bridging the gap between theory and practice

The research design of the study reported in this paper adopts the TPACK framework, which extends Shulman’s (1986, 1987) concept of Pedagogical Content Knowledge (PCK) to incorporate technological knowledge, enabling a nuanced exploration of how educators integrate technology into their teaching practices. This framework, as detailed by Mishra and Koehler (2006) and Koehler and Mishra (2008), emphasises the dynamic interactions among three core domains: content knowledge (CK), pedagogy knowledge (PK), and technology knowledge (TK). By focusing on these intersections (see Figure 1), pedagogical content knowledge (PCK), technological content knowledge (TCK), technological pedagogical knowledge (TPK) and the integrated TPACK, this model facilitates a comprehensive analysis of how generative AI tools can enhance teaching effectiveness. This framework guides the researcher in examining how the researcher herself (and others) balanced and adapted these knowledge areas to leverage GenAI technologies, ensuring alignment with subject-specific content and pedagogical goals. This approach provides a structured lens to evaluate both the potential and the challenges of integrating GenAI in educational settings, fostering innovative and effective teaching practices.

Through the application of TPACK, this study seeks to bridge the gap between GenAI’s theoretical potential and its practical implementation in educational contexts. By framing the exploration within TPACK, this research offers deep insights into how GenAI can enhance lesson preparation, teaching strategies, and educational outcomes.

Figure 1: The TPACK model conceptualised by Mishra and Koehler (2006). (Retrieved from http://tpack.org and reproduced with permission of the publisher)

As GenAI becomes increasingly integrated into higher education and other sectors, it is essential to cultivate a nuanced understanding of its applications, advantages, limitations, and inherent biases (Southworth et al., 2023). While some scholars advocate for GenAI (Guo et al., 2024; Jensen et al., 2024; Zakaria, 2024), there remains a shortage of first-hand studies that document how educators implement GenAI in their teaching practices.

This autoethnographic study aims to address this gap by offering insights into the practical use of GenAI in education. By reflecting on the lived experiences of educators, it contributes to the growing body of literature on how GenAI can transform teaching and learning in meaningful ways.

3. Research design

This autoethnography adopts a personal narrative approach, by using the researcher’s first-hand account of working as an educator in higher education, highlighting the use of GenAI for pedagogical purposes. Autoethnography was chosen for this study to illustrate these experiences vividly (Adams & Holman Jones, 2018), bringing the reader closer to the data. Member checking with other educators working within the same sector participated to provide a more balanced perspective (Candela, 2019) and a comprehensive understanding of GenAI within higher education.

3.1 Data collection

As the researcher is also the subject of the study, data were collected via reflective journaling. The researcher documented personal memories and experiences (Chang, 2016) of using GenAI tools to achieve verisimilitude (Ellis, et al., 2011). The reflective dataset was gathered between September 2023 and June 2024, coinciding with the academic year. To address the lived experience and enhance representation (Candela, 2019), member-checking from other educators was included to enrich the data, fostering a holistic and balanced view.

3.1.1 Member-checking participants

To complement the autoethnographic approach, perspectives from other educators were included through member-checking interviews. These educators were selected based on their experience using GenAI tools in their professional practice to varying degrees. This purposive sampling ensured that participants had relevant insights to offer, enriching the reflective data with diverse viewpoints.

Five member-checking interviews were conducted between July and September 2024, with each session lasting approximately one hour. The interviews followed a semi-structured format, allowing participants to reflect on their experiences.

3.1.2 Ethical considerations

All participants were full-time educators at the time of the study and provided informed consent before participating. They were assured about confidentiality and anonymity, and no identifiable information was collected during interviews. Participants were informed of their right to withdraw from the study at any point without providing a reason. The term member-checking educators or participants is used throughout this study to refer to these participants collectively. Incorporating the perspectives of member-checking educators enhanced the depth and rigour of the study, ensuring that the findings represented a more comprehensive and nuanced understanding of the use of GenAI tools in education.

Full ethical approval was granted by the research ethics committee at the Malta College of Arts, Science and Technology after showing that the research complied with the British Educational Research Association (BERA, 2018).

3.2 Data analysis

Interviews were audio recorded and transcribed automatically using Microsoft (MS) Teams. The verbatim transcripts were analysed using NVIVO-14 to facilitate organisation and retrieval of data, enhancing the rigour and transparency of the analysis. The identification of categories and themes was conducted using both inductive and deductive thematic analysis. Inductive thematic analysis allowed for the emergence of themes directly from the data without imposing preconceived categories. This approach was crucial for capturing the nuances of the main educators’ experiences with AI in higher education. On the other hand, deductive thematic analysis involved applying the TPACK educational framework to the data to validate and extend current knowledge. This thorough analysis process ensured the reliability and validity of the findings, as far as was possible, providing deep insights into the use of AI in higher education from multiple perspectives.

4. Findings

4.1 Autoethnographic findings

This section presents a rich and deep personal tapestry of autoethnographic findings, offering a reflective exploration of the researcher’s lived experiences in integrating GenAI into educational practices. Through these reflections, the narrative sheds light on the complexities, opportunities and challenges encountered while embracing this transformative technology. The findings are structured into three interconnected parts, each corresponding to the study’s research questions. Part 1 addresses RQ1, delving into the researcher’s experiences of integrating GenAI tools into their educational practices. Part 2 explores RQ2, examining how GenAI tools enhanced the efficiency and effectiveness of these practices. Finally, Part 3 reflects on RQ3, uncovering the perceived struggles and opportunities surrounding GenAI from the perspectives of both the researcher and the member-checking educators.

Together, these sections provide a nuanced understanding of the researcher’s journey with GenAI, weaving personal insights with broader educational implications. For the purpose of this study, the researcher adopts an autoethnographic approach, using ‘I’ to refer to herself and to reflect on her personal experiences and perspective on the findings.

4.1.1 Part 1: Experiencing GenAI as an educator

4.1.1.1 From a poem to a paradigm shift in embracing GenAI

I vividly recall the first time I discovered ChatGPT. It was during a lunch with colleagues following an informal department gathering in February 2023. A fellow educator enthusiastically introduced us to this new chatbot capable of generating any requested text. To our amusement, he demonstrated its capabilities by instructing the chatbot to compose a fictitious poem about two colleagues and their longstanding friendship, by providing only their names. The resulting poem was amusing, and we all pitched in to supply more details, including their hobbies and the subjects they taught. As a result, the poem became even more engaging and tailored.

Initially, it was approached with a sense of lightheartedness, until a colleague from our department raised concerns about how students might perceive this tool. This demonstration quickly shifted to a more serious discussion about the implications of such technology in academia, about mainly negative scenarios concerning student abuse of such a tool. Concerns were raised about students potentially using this tool dishonestly, for example, to generate assignments and dissertations. The conversation grew tense as many colleagues argued that such technology should be banned to preserve academic integrity. My colleagues were primarily concerned that our educational institution lacked formal regulations regarding the use of such technologies, as well as the tools necessary to detect potential plagiarism associated with their use. Consequently, we were unable to provide official guidance discouraging students from using ChatGPT at the time.

Amidst the apprehension, I found myself contemplating a different perspective. If students could harness this technology for their educational purposes, dishonest or not, could I also leverage it to enhance my teaching and preparation? As a researcher in the field of technology enhanced learning (TEL), I felt intrigued and yearned to explore this question further. This moment of curiosity and reflection marked the beginning of my journey with GenAI. With this inquisitiveness, I began to explore how different GenAI tools could make my lessons more dynamic and engaging, ultimately benefiting my instructional planning, curriculum development and my students’ learning experiences.

4.1.1.2 Experimentation and enlightenment in exploring ChatGPT’s potential

My exploration started by testing the waters, feeling a mixture of intrigue and scepticism. I explored the range of GenAI tools available, similar to ChatGPT, but ultimately chose this tool due to its user-friendly interface and pioneering status. I wanted to see what ChatGPT could do and how correct it was within the subjects I taught. I experimented cautiously, by asking the system to plan out a unit I already taught, feeding the contents of the unit randomly and asking it to plan out the unit over fifteen sessions. To my surprise, the results were identical to how I typically set the unit week by week, dividing the unit into themes, according to the topics. Typically, I assess the students over three assessments for this specific unit, and ChatGPT provided three distinct themes. However, what I found interesting was that ChatGPT’s results included an integration and application section showing me how to assess each theme in the classroom, which could also be used as an assessment. For example, one suggestion was role-playing scenarios to practice infection control techniques. I enquired further and asked for improvements in the unit, and this is when the system amazed me, by suggesting more student involvement in each session, including, for example, ‘research and present a case study on specific hygiene practices for a scenario’. Although I expected ChatGPT to provide useful suggestions on the topic, I was astonished by its deep understanding of student-centred pedagogy and the emphasis on engaging, practical, and real-world learning experiences.

After seeing how well ChatGPT planned my unit, I moved on to see how this system could help me brainstorm ideas for planning a new upcoming unit. I began by asking questions such as: ‘For a degree unit entitled Research Methods, how could I divide my lessons over fourteen sessions?’. To my surprise, ChatGPT was able to generate creative and engaging lesson structures that I could tailor to the specific needs of my unit. Even without giving the unit contents, the system gave me a feasible plan that aligned with the unit’s criteria, with a consistent flow from one topic to the next. I continuously experimented, by providing cues, and appreciated how I could edit the responses the system gave. By feeding more data to the system, the feedback became more specific, and the reactions were more to my needs. For example, in one instance, I input the following: ‘In session 6, the lesson on data collection methods, I additionally want to cover collecting self-data through autoethnography’. As a response, the system rewrote the plan by including the research method and technique in the session, with a step-by-step plan. I began to notice that ChatGPT has a memory, as it built on the information I had shared earlier to shape its responses to my new questions. This gave our interactions a sense of continuity, almost like it was learning from me as we went along. This was not only time-saving, it brought a fresh perspective to my teaching approach. I felt as if I had a second pair of eyes checking over my work, helping me to not forget a detail important to the topic.

As I became more confident using this tool, I sought help from ChatGPT to create layout plans for presentations. By feeding in the topic I wanted to cover, and adding in cues of keywords, I found that the system presented information on how to construct each slide and the order and sequence contributed to the overarching topic. An aspect I found comforting was if I did not agree with the flow/order that ChatGPT generated, or wished to provide additional input, I could feed my concern to the system, and it would immediately start over, including my amendment.

4.1.2 Part 2: Enhancing teaching practices with GenAI

4.1.2.1 Transforming the teaching game through ChatGPT

Later, when ChatGPT 4.0 was released, I noticed a significant improvement in both the speed and quality of the responses. This version had a quicker response rate, plus the content was more accurate and insightful. For instance, when generating lesson plans, it not only provided accurate content but also suggested creative, student-centred activities tailored to specific learning outcomes with nuanced explanations of how such activities would reach learning goals for different learner types. This combination of speed and enhanced explanations made my overall experience far more effective and rewarding. I began using ChatGPT to develop more engaging assessments, and it redefined my approach. My experience creating assessments before this journey was a labour-intensive task, particularly the challenge of crafting interesting and varied questions year after year. ChatGPT transformed this process by helping me refine my assessments and generate diverse questions that moved past simple recall but called upon students’ critical thinking and practical application. This was something I struggled with, as I taught diverse cohorts across multiple courses. My hands-on experience revealed that using keywords such as ‘Refine’, ‘Rework’, ‘Adapt’, and ‘Revise’, I was able to leverage the system to create multiple versions of past papers, tailoring them to different cohorts and aligning them more closely to the students’ fields of study. I also used terms like ‘Generate’ to create new questions. I could easily amend the questions as needed by asking the system to swap questions or even change the mode of examination entirely. This experience enriched my work and motivated me to approach material adaptation with more creativity.

Beyond lesson plans and assessments, ChatGPT also became an invaluable tool for generating classwork ideas. I vividly recall a moment in the classroom when, after finishing my presentation, the student’s absence of questions made me suspect that the material might not have been fully comprehended. I turned to ChatGPT, asking for a short classwork exercise on a specific topic by inputting just four keywords related to the topic, and it generated a short exercise with five questions. This exercise was enough for students to reflect on the lesson and find solutions. It provided an opportunity for students to ask questions during the classwork, revealing the areas where they needed further explanation. The experience demonstrated how powerful this tool could be in a classroom setting, enhancing both teaching and learning. This testing and implementing phase left me in awe as my preparation time decreased, the creativity of my work deepened and my interest in GenAI increased. I began exploring additional GenAI tools that could enhance my presentations. Previously, I had started utilising the ‘Design’ feature in MS PowerPoint, discovering its potential as a GenAI tool that facilitates the creation of slide images, offering suggestions for design elements. However, one tool I found particularly compelling was Microsoft’s Copilot. This tool, similar to ChatGPT’s ability to generate text for information retrieval, offered the added functionality of producing original images. I experimented with Copilot by requesting images that I commonly use in my teaching, such as illustrations of body structures and cells. However, not all generated images were entirely accurate. For example, I recall asking for an image of a eukaryotic cell, but the image lacked identifiable components. Despite this setback, Copilot excelled in creating simpler images. For example, when I requested a depiction of a hospital setting where infection control policies were being implemented, the tool generated a clear and effective visual. These images proved highly useful for presentations, allowing me to illustrate concepts precisely as I envisioned them. Before utilising this tool, I underwent the time-consuming task of sifting through numerous pages of Google Images or Stock Images, all the while being concerned about potential copyright violations. However, Copilot, when given specific cues, efficiently generated the desired image within seconds, alleviating the concerns related to copyright infringement.

Using GenAI has also significantly expanded my ability to contribute to educational research. While I had been utilising Grammarly for several years for proofreading, their introduction of GenAI in Grammarly marked a noticeable shift in this writing assistance tool. The advanced functionalities greatly enhanced both the effectiveness and sophistication of my writing. As I became more confident in ChatGPT’s functionalities, I started using it to check my writing for clarity. I found this tool to be instrumental in refining my writing skills by helping me produce concise and focused content. Although English is my first language, this tool has enabled me to articulate my ideas more efficiently, allowing me to get to the point more quickly. Additionally, I have used the system to evaluate whether my writing style is engaging to an academic audience and to receive suggestions for improvement. For instance, when composing this research paper, I employed ChatGPT to rephrase complex arguments more straightforwardly, ensuring clarity without sacrificing depth; this was something I had previously struggled with. The tool also assisted in structuring my arguments logically, which was invaluable in improving the overall coherence of the paper. This iterative process not only enhanced the quality of my writing but also provided me with insights into how to better tailor my work to meet academic standards, with a virtual proofreader available to advise and teach me how to be a better writer. In addition, I utilised ChatGPT to gain a deeper understanding of key terms and relevant frameworks, allowing me to critically engage with the literature and refine my approach to research.

4.1.3 Part 3: Challenges and opportunities in GenAI adoption

My overall experience with GenAI left me feeling so supported that I felt that I needed to share my experiences with others. When meeting colleagues, I actively sought opportunities to ask them about their experience with GenAI tools. Specifically, I was interested in whether they were using GenAI for lesson preparation and other academic endeavours, and how. However, to my surprise, many were unaware that GenAI could be used for this reason. More importantly, I was met with sentiments of disapproval. Investigating deeper through informal conversations in the staff room, I overheard educators’ criticism and pushback towards AI, furthermore, blaming AI for students’ overnight work improvements. Overall, there was an unmentionable feeling surrounding this topic, which made me feel self-conscious to bring up my positive experiences, almost ashamed for using this technology. It felt like a taboo to discuss unless so in a negative sense, making me fear being ridiculed. These feelings made me realise just how deeply rooted the scepticism and fear surrounding AI in education is. The distrust was creating a barrier that suppressed open discussion and prevented us from exploring the potential benefits that AI could bring to learning. The irony was that while AI was being criticised for its supposed role in enabling shortcuts, it was also being overlooked as a tool that could empower educators and students, through enhancing creativity and offering personalised learning experiences. On the other hand, GenAI tools were much easier to discuss with students, as they seemed more open to learning and exploring GenAI tools. This realisation pushed me to share my hands-on experience with students and research my personal journey, as to help myself understand my growth. Even more so, I felt inclined to help educators overcome their fears and misconceptions that are hindering us from embracing innovative methods that could genuinely improve educational outcomes. Expectedly, even finding educators to participate in this study for member-checking purposes was a struggle. Educators approached were not all willing to share their experiences using GenAI out of fear of being recognised and frowned upon by our peers. When approached, one educator replied that they feared being identified and deemed less of a professional. What was it about using a virtual assistant that can make us feel guilt-ridden and threatened?

4.1.3.2 Leading the way from resistance to ethical GenAI integration

Recognising the importance of this reality, I dedicated lessons specifically to instruct my degree students on the appropriate use of GenAI for educational purposes. While I do not claim to be an expert, I actively attended short courses on AI for education to help me become more versed, as I believed students needed to be directed on how to utilise GenAI efficiently and ethically as a tool for learning, to avoid misuse. At the outset of the lesson, my students were surprised and somewhat confused by my encouragement of its use. Students expressed that other educators had strictly informed them never to use AI whatsoever. However, I emphasised that, when used correctly, GenAI tools can significantly enhance their educational experience. I showed them different tools I had used myself, showing them how it could help them summarise study notes, help them come up with creative ideas for projects, check their writing and even help them learn concepts that they did not understand in the classroom. These sessions were productive, and the students’ feedback inspired me to keep sessions allocated specifically for this use, as I was able to convey that GenAI can be used ethically and responsibly to support their academic growth. When later I was writing a revised unit on research, I included a criterion about AI-assisted research methods and, to my surprise, it was accepted by the curriculum department. Today, the research methods unit I teach has a component on the responsible and effective use of GenAI in research.

Looking back to the beginning of my journey, I too had concerns about the ethics of using AI, similar to my colleagues. I worried about over-reliance on technology and the potential loss of the human touch in teaching. However, I realised that these tools were not replacing me but augmenting my capabilities and this was the message I wanted to pass on to my students. In closing, after my experience, I acknowledge that GenAI, holds immense potential in educational settings, but its value depends largely on how it is integrated into the learning process. Hopefully, we can guide students on the ethical and effective use of these tools; however, first I feel educators need to support each other and let go of preconceptions so that we can harness the full benefits of technology while fostering a deeper understanding of their academic responsibilities.

4.2 Findings from member-checking participants

This section concludes by aligning perspectives from the member-checking participants, which situates the researcher’s unique journey as a GenAI advocate alongside the perspectives of participating educators. While their views partially validated the researcher’s findings, they also highlighted ongoing scepticism and barriers to adoption. This alignment underscores the broader challenges and potential for growth in integrating GenAI into education.

4.3 Aligning perspectives

The researcher’s journey with GenAI was uniquely transformative, evolving from initial scepticism to becoming an advocate and ambassador for its ethical integration in education. Member-checking interviews provided valuable perspectives that partially validated this experience. While the participating educators acknowledged that integrating GenAI is a learning process, they also confirmed its opportunities of enhancing the efficiency and effectiveness of teaching duties and research; their experiences from the researcher differed significantly.

Unlike the researcher, who embraced an advocative role, the member-checking educators largely expressed ongoing scepticism. Fears about GenAI ranged from academic integrity, misuse and accountability, stemming from a lack of formal training and the absence of clear institutional policies. These factors appeared to hinder their ability to fully embrace or champion GenAI tools in their practices. Nonetheless, their involvement confirmed that GenAI has the potential to support educators’ work when approached thoughtfully, highlighting the need for increased awareness, guidance, collaboration and training to bridge this gap.

5. Discussion

The findings of this study are organised around three central themes and analysed using the TPACK framework to understand better the integration of GenAI for teaching and learning contexts. By applying this TPACK framework, the discussion explores how educators navigate technological, pedagogical, and content knowledge within the GenAI context, revealing opportunities and challenges. The three themes identified are: (1) Exploration and adoption of GenAI for enhanced teaching; (2) Scepticism leading to resistance towards GenAI in education; and (3) Advocating for ethical and responsible uses of GenAI. Insights from member-checking educators are included, ensuring a balanced representation of experiences and informing a holistic understanding of GenAI in education. (Please note that, in the discussion, the pronoun ‘her/she’ is used to refer to the main researcher.)

5.1 Theme 1: Exploration and adoption of GenAI for enhanced teaching

This theme captures the researcher’s positive journey in exploring and adopting GenAI, reflecting the developmental progression from initial curiosity to advocacy. There were three phases - 1) Exploration, 2) Learning, and 3) Dissemination - that illustrated the iterative nature of integrating GenAI into education practices and directly address the first and second research questions.

In the exploration phase, the researcher began by delving into various GenAI tools, such as ChatGPT and Microsoft Copilot, discovering their potential to streamline lesson planning, assessment creation, and student engagement, a perspective supported by recent studies (Jensen et al., 2024; Zakaria, 2024). This phase was marked by excitement and experimentation for further engagement with these tools, as illustrated by a participant’s remark:

“Initially, when I started using AI, I didn’t know what to expect. The process was slow, but as I realised how productive I can be, I was euphoric”.

In the learning phase, the researcher deepened her engagement, applying GenAI hands-on, enabling the researcher and other educators to develop TK. This iterative process enhanced CK, advancing lesson planning, assessment design, and research activities (Rahman & Watanobe, 2023). One participant highlighted the practical benefits:

“My journey implementing GenAI was positive as it impacted my organisational strategy, especially time management and instructional content design and planning. It took time to adapt, but now I also find it important for research and data analysis”.

However, the learning phase also revealed challenges, with some educators feeling apprehensive due to limited technical expertise:

“I am a bit apprehensive to use AI as I’m not as tech savvy as I wish to be, so I see having to learn to navigate GenAI tools, a bit of a hassle”.

This aligns with Su and Yang (2023), who emphasised the importance of targeted training to overcome such barriers. The findings underscore how skill-building fosters confidence and enables educators to take ownership of their learning (Delia & Lee, 2024), adopting a learner-centred approach to understanding and navigating GenAI tools.

In response to RQ2, the findings indicate that the exploration and learning phases of engaging with GenAI significantly enhance the educator’s practices. These phases foster a sense of support and empowerment, as the technology alleviates workload pressures and streamlines time-intensive tasks. One of the most notable contributions of GenAI was its ability to provide fresh ideas and perspectives for teaching and presentation materials. For instance, GenAI facilitated the creation of more visually engaging MS PowerPoint presentations and offered innovative approaches to lesson planning. This adaptability enabled the researcher to refine content to align with diverse course contexts, ensuring relevance and inclusivity. One participant indicated:

“In the past, when creating a worksheet, I would have targeted it towards the average student, which meant that the struggling student would find it too hard, and the top student would find it too easy. Now that worksheet creation takes less time, I can easily create variations of it so that every student would find it equally challenging”.

These findings align with existing research emphasising the transformative potential of GenAI in education. Studies highlight that AI-driven tools enable educators to focus more on critical pedagogical and interactive tasks by automating routine processes (Rahman & Watanobe, 2023; Su & Yang, 2023). Additionally, the creative outputs of GenAI tools contribute to more dynamic and engaging classroom environments, fostering deeper student engagement (Baidoo-Anu & Ansah, 2023).

Overall, the sense of support, innovation, and efficiency experienced during the exploration phase underscored the transformative role of GenAI tools in enhancing both the effectiveness and efficiency of educational practices. These tools empower educators to optimise their time, creativity, and pedagogical strategies, ultimately improving the quality of teaching and learning.

Unlike the earlier phases, the dissemination phase focuses on the researcher’s role as an advocate for GenAI, sharing through workshops, mentoring, and educational research. While the member-checking educators recognised GenAI’s utility in teaching, they were less inclined to actively promote its adoption within their communities. This divergence may stem from the researcher’s unique position as a TEL scholar, driving a commitment to advancing technological knowledge.

Through the TPACK framework, the dissemination phase demonstrates the interplay between TK and PK. However, the findings highlight the need for systemic initiatives to cultivate a professional culture that values knowledge-sharing, particularly for educators without a TEL background.

5.2 Theme 2: Scepticism leading to resistance towards GenAI in education

The second theme captures scepticism and resistance towards GenAI in education, providing insights that partially address RQ3. While Theme 1 identified technological knowledge as a barrier, this theme delves deeper into the emotional and perceptual aspects of scepticism experienced by both the researcher and member-checking participants. The findings reveal that the researcher initially experienced scepticism towards GenAI; however, as the exploration phase commenced, her scepticism faded, unlike those of the member-checking educators. One educator shared:

“I use it (ChatGPT) when I need to, but do not promote it, as I know others think it’s wrong. Maybe they are right, how can we tell students not to use it and then use it myself”.

This statement highlights a moral tension, reflecting the bidirectional relationship between scepticism and resistance: scepticism fosters resistance and resistance, in turn, deepens scepticism. For member-checking participants, this hesitancy acted as a protective mechanism against perceived risks, particularly ethical concerns surrounding GenAI, as noted by Alwaqdani (2024). Scepticism often stemmed from a lack of TPK, particularly among educators without a background in TEL. These educators struggled to reconcile the integration of AI tools with existing teaching methodologies, as described by Koehler and Mishra (2009). Concerns about eroding fundamental educational values, including critical thinking and creativity, were pervasive. One educator remarked:

“I have not felt inclined to explore more tools apart from the free version of Grammarly and PowerPoint Designer. I think that in a way we are deskilling ourselves, for example, I use Grammarly instead of thinking of the right sentence structure or the proper grammar I should use or when I use Designer (PowerPoint) instead of being creative and making an effort to design my slides”.

This sentiment underscores the perception that GenAI reliance could lead to a decline in key skills like grammar and design creativity (Farrokhnia, 2024), raising questions about technology’s alignment with education’s core mission (Geerling, 2023). Beyond skill erosion, educators expressed apprehension about GenAI disrupting traditional pedagogy and undermining academic integrity (Dwivedi, 2024). Concerns about introducing students to GenAI’s capabilities too early further fuelled resistance. One educator explained:

“I use AI to help me create assignments, but I do not dare use ChatGPT in the classroom. Sub-consciously it may be because I wouldn’t want students to be aware of its power too early”.

This cautious approach suggests that while GenAI is leveraged for administrative tasks, it is withheld from students due to fears it could undermine the learning process. These concerns reflect gaps in both TPK and TCK, as educators struggle to balance innovation with maintaining pedagogical and academic standards. For example, another educator voiced concerns about student misuse:

“My biggest worry is that students would submit material which they do not understand”.

This highlights the challenge of integrating GenAI into teaching without compromising academic rigour, echoing findings by Sullivan et al. (2023) and Yu (2023). Fears that GenAI might allow students to bypass meaningful engagement further underscore this tension.

Nevertheless, these concerns point to the importance of increased exposure and hands-on experience with GenAI tools. As Chiu (2023) notes, practical engagement can help educators build confidence and reduce resistance. Participants who shifted their perspectives after using GenAI suggest that resistance may be mitigated through targeted training and support, bridging gaps in TPK and TCK. These results align with the study as the main researcher’s initial scepticism diminished through hands-on experimentation. Coming from a TEL background with prior experience implementing other educational tools, she was able to build confidence and recognise the potential of GenAI through practical engagement.

5.3 Theme 3: Advocating for ethical and responsible uses of GenAI

To fully answer RQ3, this theme complements the insights from the previous theme, which explored the struggles related to scepticism and resistance towards GenAI. While Theme 2 delved into the challenges educators face, this theme focuses on the opportunities GenAI offers when used responsibly and ethically, presenting a balanced perspective that answers RQ3 in its entirety. Here, the emphasis shifts to how educators can leverage GenAI as a transformative tool, provided its integration aligns with pedagogical goals and ethical considerations. By demonstrating practical applications of the TPACK framework, this theme illustrates how the alignment of TK, PK, and CK can mitigate struggles and unlock opportunities.

The researcher, through the successful integration of GenAI in her teaching, advocated for a holistic and responsible approach, emphasising its role as a tool to enrich learning rather than a shortcut which supports the alignment of TK, PK, and CK in ensuring effective GenAI use. Member-checking educators in the study did not mirror this advocacy, acknowledging GenAI’s value as a supportive teaching tool, whilst still remaining sceptic. One member-checking educator remarked:

“I always check my content to see that it is correct. It is like having a teaching assistant, but still I find a lot of gibberish at times. I cannot imagine what students would make out of it”.

This highlights how GenAI can enhance content delivery when used judiciously, as suggested by Guo et al. (2024). The responsible integration of GenAI aligns with PK by ensuring that traditional pedagogical goals, such as fostering critical thinking and student engagement, remain central. The findings also emphasise the importance of institutional frameworks to guide ethical AI use. One member-checking educator advocated for clear policies, stating:

“In the future, I wish to see a clear policy on the usage of GenAI and standardisation”.

This perspective underscores the necessity for policy reforms and ethical guidelines, aligning with the academic standards discussed by Perkins and Roe (2024). Such frameworks can provide educators and students with clarity on appropriate GenAI applications, reducing ambiguity and fostering trust in its potential as a transformative educational tool.

By advocating ethical AI use and ensuring alignment with the TPACK framework, the findings suggest that the future of GenAI in education lies in its ability to empower educators and learners alike, without compromising academic values. This reinforces the opportunities inherent in GenAI, provided its implementation is guided by robust ethical considerations and supported by clear institutional policies.

6. Conclusion

The study reported in this paper explored the integration of GenAI tools into educational practices through a personal autoethnographic lens, offering a rich, contextual understanding of the experiences, opportunities, and challenges faced by educators. By addressing the three research questions, this study has revealed the transformative potentials and the complexities associated with GenAI in teaching.

In response to RQ1, the experiences of educators integrating GenAI into their pedagogy suggest that these tools can foster creativity, efficiency, and innovation in lesson planning, classroom teaching practices, and educational research. However, these positive outcomes are tempered by concerns about technological competence, the risk of pedagogical disruption, and the ethical challenges posed by the use of AI tools. Regarding RQ2, the findings demonstrate that GenAI tools can significantly enhance the efficiency and effectiveness of educators’ practices. These tools streamline preparatory tasks, support personalised learning, and assist in content creation. However, the integration of such tools requires a careful balance to avoid overreliance on technology that may undermine the educator’s role as a critical and reflective practitioner. RQ3, which explores the perceived struggles and opportunities, highlighted that educators face difficulties such as concerns over ethical issues like privacy and academic integrity while overcoming the steep learning curve of AI tools. At the same time, opportunities for professional growth, the fostering of innovative teaching methods, and the potential for collaborative learning emerge as key benefits from the use of GenAI in education.

While the findings contribute valuable insights into integrating GenAI, the study acknowledges limitations inherent in the autoethnographic approach. The reliance on a single dataset based on memory and personal experience may introduce bias and limit generalisability. Additionally, while reflexivity and member-checking were employed to enhance credibility, subjectivity remains challenging. Moreover, the inherent risks of autoethnography, including unintentional exposure of the researcher and participants, highlight the complexity of this methodological approach.

In light of these findings, several recommendations are proposed. First, continuous professional development is crucial to strengthening educators’ technological knowledge, ensuring a balanced and thoughtful integration of GenAI. This integration should complement, rather than replace, traditional pedagogical methods. Second, a proactive yet cautious approach to the introduction of GenAI in classrooms is recommended, encouraging both educators and students to engage critically with these tools. From an institutional perspective, the development of ethical guidelines and updated academic integrity policies are essential to ensure the responsible and ethical use of GenAI in education. Lastly, fostering a culture of innovation and cross-disciplinary collaboration will enable educational institutions to more effectively harness the benefits of GenAI while upholding academic standards and ethical principles.

Ultimately, this study emphasises the evolving role of AI in education and the need for educators and institutions to remain vigilant and reflective about its impact on teaching and learning. As the TEL field continues to evolve, the integration of GenAI offers exciting possibilities to innovate and improve educational practices. However, its thoughtful implementation is critical, ensuring that GenAI enriches, rather than disrupts, the educational process. This study calls for ongoing reflections within the TEL field, encouraging educators and institutions to critically engage with AI tools, thereby fostering an educational environment that values both technological innovation and ethical responsibility.


About the author

Cassandra Sturgeon Delia, Centre for Learning and Employability (CLE), The Malta College of Arts, Science and Technology (MCAST), Poala, Malta

Cassandra Sturgeon Delia

Cassandra Sturgeon Delia is a senior lecturer at MCAST with experience in higher education, research, and healthcare. She holds a PhD in Technology Enhanced Learning and E-Research from Lancaster University, where her research delves into the intersection of educational technology and teaching practices. Cassandra’s work focuses on educators’ experiences with technology, networked learning, and the application of autoethnography as a methodology. Her diverse career in teaching, research, and mentorship informs her scholarly work and innovative initiatives in education. Recently appointed Social Media Associate Editor for Nursing in Critical Care (NICC), she is leveraging her expertise in educational technology to enhance the journal’s digital presence and foster knowledge exchange in the critical care nursing community.

Email: [email protected]

ORCID: 0000-0002-9097-4786

LinkedIn: https://www.linkedin.com/in/cassandra-sturgeon-delia-phd-1ba98016a/

ResearchGate: https://www.researchgate.net/profile/Cassandra-Sturgeon-Delia

Google Scholar: https://scholar.google.com/citations?user=cbyy_KgAAAAJ&hl=en

Article information

Article type: Full paper, double-blind peer review.

Publication history: Received: 23 October 2024. Revised: 16 December 2024. Accepted: 19 December 2024. Online: 27 January 2025.

Cover image: Badly Disguised Bligh via flickr.


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