Tag: Reflective Practice

Teaching, Learning, Assessment and GenAI: Moving from Reaction to Intentional Practice

GenAI:N3
By Dr Hazel Farrell & Ken McCarthy, South East Technological University & GenAI:N3
Estimated reading time: 7 minutes
A digital illustration depicting the intersection of technology and higher education. On the left, a glowing, translucent human brain composed of neural networks rises from an open, illuminated book. On the right, a group of educators and professionals sit in a circle at a glowing round table, engaged in a collaborative discussion. The background features subtle academic symbols like a graduation cap and a chalkboard, all set in a futuristic, tech-enabled environment. Image (and typos) generated by Nano Banana.
Moving from reaction to intentional practice: Exploring the collaborative future of Generative AI in higher education through human-led dialogue and pedagogical reflection. Image (and typos) generated by Nano Banana.

Generative AI has become part of higher education with remarkable speed.

In a short period of time, it has entered classrooms, assessment design, academic writing, feedback processes, and professional workflows. For many educators, its arrival felt sudden and difficult to make sense of, leaving little space to pause and consider what this shift means for learning, teaching, and academic practice.

Initial responses across the sector have often focused on risk, regulation, and control. These concerns are understandable. Yet they only tell part of the story. Alongside uncertainty and anxiety, there is also curiosity, experimentation, and a growing recognition that GenAI raises questions that are fundamentally pedagogical rather than purely technical.

On 21 January, we are delighted to host #LTHEchat to explore these questions together and to move the conversation from reaction towards more intentional, reflective practice.

The discussion will be grounded in the Manifesto for Generative AI in Higher Education, and informed by the wider work of GenAI:N3, a national initiative in Ireland supporting collaborative engagement with generative AI across higher education.

GenAI:N3: A Collaborative Project for the Sector

GenAI:N3 is a national network that was established in Ireland as part of the N-TUTORR programme, to support technological higher education institutions as they responded to the rapid emergence of generative AI. Rather than focusing on tools or technical solutions, the project centres on people, practice, and shared learning.

At its core, GenAI:N3 aims to build institutional and sectoral capacity by creating spaces where educators, professional staff, and leaders can explore GenAI together. Its work is grounded in collaboration across institutions and disciplines, recognising that no single university or role has all the answers.

The project focuses on several interconnected areas:

  • Supporting communities of practice where staff can share experiences, challenges, and emerging approaches
  • Encouraging critical and reflective engagement with GenAI in teaching, learning, assessment, and professional practice
  • Exploring the ethical, social, and institutional implications of GenAI, including questions of power, inclusion, sustainability, and academic judgement
  • Developing shared resources, events, and conversations that help the sector learn collectively rather than in isolation

GenAI:N3 is not about accelerating adoption for its own sake. It is about helping institutions and individuals make informed, values-led decisions that are aligned with the purposes of higher education.

The Manifesto as a Shared Thinking Space

The Manifesto for Generative AI in Higher Education emerged from this collaborative context. It did not begin as a formal deliverable or a policy exercise. Instead, it took shape gradually through workshops, conversations, reflections, and recurring questions raised by staff and students across the sector.

What became clear was a need for a shared language. Not a framework that closed down debate, but a set of statements that could hold complexity, uncertainty, and difference.

The Manifesto brings together 30 short statements organised across three themes:

  • Rethinking teaching and learning
  • Responsibility, ethics, and power
  • Imagination, humanity, and the future

It is intentionally concise and deliberately open. It does not offer instructions or compliance rules. Instead, it invites educators and institutions to pause, reflect, and ask what kind of learning we are designing for in a world where generative tools are readily available.

One of its central ideas is that GenAI does not replace thinking. Rather, it reveals the cost of not thinking. In doing so, it challenges us to look beyond surface solutions and to engage more deeply with questions of purpose, judgement, and educational values.

Why These Conversations Matter Now

Much of the early discourse around GenAI has centred on assessment integrity and detection. While these issues matter, they risk narrowing the conversation too quickly.

GenAI does not operate uniformly across disciplines, contexts, or learning designs. What is productive in one setting may be inappropriate in another. Students experience this inconsistency acutely, particularly when institutional policies feel disconnected from everyday teaching practice.

The work of GenAI:N3, and the thinking captured in the Manifesto, keeps this complexity in view. It foregrounds ideas such as transparency as a foundation for trust, academic judgement as something that can be supported but not automated, and ethical leadership as an institutional responsibility rather than an individual burden.

These ideas play out in very practical ways, in curriculum design, in assessment briefs, in conversations with students, and in decisions about which tools are used and why.

Why #LTHEchat?

#LTHEchat has long been a space for thoughtful, practice-led discussion across higher education. That makes it an ideal forum to explore generative AI not simply as a technology, but as a catalyst for deeper pedagogical and institutional reflection.

This chat is not about promoting a single position or reaching neat conclusions. Instead, it is an opportunity to surface experiences, tensions, and emerging practices from across the sector.

The questions we will pose are designed to open up dialogue around issues such as abundance, transparency, disciplinary difference, and what it means to keep learning human in a GenAI-rich environment.

An Invitation to Join the Conversation

Whether you are actively experimenting with generative AI, approaching it with caution, or still forming your views, your perspective is welcome.

Bring examples from your own context. Bring uncertainties and unfinished thinking. The Manifesto itself is open to use, adapt, and challenge, and GenAI:N3 continues to evolve through the contributions of those engaging with its work.

As the Manifesto suggests, the future classroom is a conversation. On 21 January, we hope you will join that conversation with us through #LTHEchat.

Links

LTHE Chat Website: https://lthechat.com/

LTHE Chat Bluesky: https://bsky.app/profile/lthechat.bsky.social

Dr Hazel Farrell

GenAI Academic Lead
SETU

Hazel Farrell has been immersed in the AI narrative since 2023 both through practice-based research and the development of guidelines, frameworks, tools, and training to support educators and learners throughout the HE sector. She led the national N-TUTORR GenAI:N3 project which was included in the EDUCAUSE 2025 Horizon Report as an exemplar of good practice. She is the SETU Academic Lead for GenAI and Chair of the university’s GenAI Steering Committee. The practical application of GenAI provides a strong foundation for her research, with student engagement initiatives for creative disciplines at the forefront of her work. Hazel recently won DEC24 Digital Educator Award for her GenAI contributions to the HE sector. She has presented extensively on a variety of GenAI related topics and has several publications in this space.

Ken McCarthy

Head of Centre for Academic Practice
SETU

Ken McCarthy is the Head of the Centre for Academic Practice at SETU, Ken leads strategic initiatives to enhance teaching, learning, and assessment across the university. He works with academic staff, professional teams, and students to promote inclusive, research-informed, and digitally enriched education. He is the current vice-president of ILTA (Irish Learning Technology Association) and was previously the university lead for the N-TUTORR programme. He has a lifelong interest in technology and education and combines this in his professional role. He has written and presented on technology enhanced learning in general and in GenAI in particular over the past number of years.

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Dr. Strange-Syllabus or: How My Students Learned to Mistrust AI and Trust Themselves

GenAI:N3
by Tadhg Blommerde – Assistant Professor, Northumbria University
Estimated reading time: 5 minutes
A stylized image featuring a character resembling Doctor Strange, dressed in his iconic attire, standing in a magical classroom setting. He holds up a glowing scroll labeled "SYLLABUS." In the foreground, two students (one Hispanic, one Black) are seated at a table, working on laptops that display a red 'X' over an AI-like interface, symbolizing mistrust of AI. Above Doctor Strange, a glowing, menacing AI entity with red eyes and outstretched arms hovers, presenting a digital screen, representing the seductive but potentially harmful nature of AI. Magical, glowing runes, symbols, and light effects fill the air around the students and the central figure, illustrating complex learning. Image (and typos) generated by Nano Banana.
In an era dominated by AI, educators are finding innovative ways to guide students. This image, inspired by a “Dr. Strange-Syllabus,” represents a pedagogical approach focused on fostering self-reliance and critical thinking, helping students to navigate the complexities of AI and ultimately trust their own capabilities. Image (and typos) generated by Nano Banana.

There is a scene I have witnessed many times in my classroom over the last couple of years. A question is posed, and before the silence has a chance to settle and spark a thought, a hand shoots up. The student confidently provides an answer, not from their own reasoning, but read directly from a glowing phone or laptop screen. Sometimes the answer is wrong and other times it is plausible but subtly wrong, lacking the specific context of our course materials. Almost always the reasoning behind the answer cannot be satisfactorily explained. This is the modern classroom reality. Students arrive with generative AI already deeply embedded in their personal lives and academic processes, viewing it not as a tool, but as a magic machine, an infallible oracle. Their initial relationship with it is one of unquestioning trust.

The Illusion of the All-Knowing Machine

Attempting to ban this technology would be a futile gesture. Instead, the purpose of my teaching became to deliberately make students more critical and reflective users of it. At the start of my module, their overreliance is palpable. They view AI as an all-knowing friend, a collaborator that can replace the hard work of thinking and writing. In the early weeks, this manifests as a flurry of incorrect answers shouted out in class, the product of poorly constructed prompts fed into (exclusively) ChatGPT, and a complete faith in the response it generated. It was clear there was a dual deficit: a lack of foundational knowledge on the topic, and a complete absence of critical engagement with the AI’s output.

Remedying this begins not with a single ‘aha!’ moment, but through a cumulative, twelve-week process of structured exploration. I introduce a prompt engineering and critical analysis framework that guides students through writing more effective prompts and critically engaging with AI output. We move beyond simple questions and answers. I task them with having AI produce complex academic work, such as literature reviews and research proposals, which they would then systematically interrogate. Their task is to question everything. Does the output actually adhere to the instructions in the prompt? Can every claim and statement be verified with a credible, existing source? Are there hidden biases or a leading tone that misrepresents the topic or their own perspective?

Pulling Back the Curtain on AI

As they began this work, the curtain was pulled back on the ‘magic’ machine. Students quickly discovered the emperor had no clothes. They found AI-generated literature reviews cited non-existent sources or completely misrepresented the findings of real academic papers. They critiqued research proposals that suggested baffling methodologies, like using long-form interviews in a positivist study. This process forced them to rely on their own developing knowledge of module materials to spot the flaws. They also began to critique the writing itself, noting that the prose was often excessively long-winded, failed to make points succinctly, and felt bland. A common refrain was that it simply ‘didn’t sound like them’. They came to realise that AI, being sycophantic by design, could not provide the truly critical feedback necessary for their intellectual or personal growth.

This practical work was paired with broader conversations about the ethics of AI, from its significant environmental impact to the copyrighted material used in its training. Many students began to recognise their own over-dependence, reporting a loss of skills when starting assignments and a profound lack of satisfaction in their work when they felt they had overused this technology. Their use of the technology began to shift. Instead of a replacement for their own intellect, it became a device to enhance it. For many, this new-found scepticism extended beyond the classroom. Some students mentioned they were now more critical of content they encountered on social media, understanding how easily inaccurate or misleading information could be generated and spread. The module was fostering not just AI literacy, but a broader media literacy.

From Blind Trust to Critical Confidence

What this experience has taught me is that student overreliance on AI is often driven by a lack of confidence in their own abilities. By bringing the technology into the open and teaching them to expose its limitations, we do more than just create responsible users. We empower them to believe in their own knowledge and their own voice. They now see AI for what it is: not an oracle, but a tool with serious shortcomings. It has no common sense and cannot replace their thinking. In an educational landscape where AI is not going anywhere, our greatest task is not to fear it, but to use it as a powerful instrument for teaching the very skills it threatens to erode: critical inquiry, intellectual self-reliance, and academic integrity.

Tadhg Blommerde

Assistant Professor
Northumbria University

Tadhg is a lecturer (programme and module leader) and researcher that is proficient in quantitative and qualitative social science techniques and methods. His research to date has been published in Journal of Business Research, The Service Industries Journal, and European Journal of Business and Management Research. Presently, he holds dual roles and is an Assistant Professor (Senior Lecturer) in Entrepreneurship at Northumbria University and an MSc dissertation supervisor at Oxford Brookes University.

His interests include innovation management; the impact of new technologies on learning, teaching, and assessment in higher education; service development and design; business process modelling; statistics and structural equation modelling; and the practical application and dissemination of research.

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