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

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.

Keywords


Student Success Leaders Worry About Affordability, AI and Diversity


A composite visual showing three distinct, stylized icons representing major challenges: A padlock with dollar signs (Affordability), a swirling digital vortex or chatbot logo (AI), and a group of varied silhouettes (Diversity). All three are converging on a single, glowing student figure, symbolizing the multiple pressures on student success leaders. Image (and typos) generated by Nano Banana.
Triple threat to student success: Leaders in higher education are currently grappling with the complex and intertwined challenges of making college affordable, integrating AI responsibly, and ensuring robust diversity and inclusion across their institutions. Image (and typos) generated by Nano Banana.

Source

Inside Higher Ed

Summary

This article examines the concerns expressed by student-success leaders across U.S. higher education institutions, reflecting a convergence of affordability challenges, diversity commitments and the accelerating influence of generative AI. While administrators generally maintain confidence in institutional missions, they report increasing difficulty in evaluating authentic student engagement and learning outcomes due to widespread AI use. AI-assisted work can obscure students’ actual competencies, making early intervention and personalised support more complex. Leaders warn that inequitable access to advanced AI tools and differences in digital literacy may widen existing gaps for underrepresented groups. These concerns extend beyond teaching and assessment policies to broader institutional planning, prompting calls for staff training, student guidance frameworks and integrated AI governance strategies. The article suggests that institutions must adopt more holistic responses that acknowledge AI’s influence on retention, equity, affordability and long-term student success. AI is no longer a marginal pedagogical issue but an influential variable in strategic decision-making.

Key Points

  • AI seen as major pressure alongside affordability and DEI.
  • AI affects measurement of engagement and outcomes.
  • Risks of widening equity gaps.
  • Need for proactive policy.
  • AI now strategic issue, not just pedagogical.

Keywords

URL

https://www.insidehighered.com/news/students/academics/2025/11/06/student-success-leaders-worry-about-affordability-ai-dei

Summary generated by ChatGPT 5.1


Students Who Lack Academic Confidence More Likely to Use AI


In a modern university library setting, a young female student with a concerned expression is intently focused on her laptop. A glowing holographic interface floats above her keyboard, displaying "ESSAY ASSIST," "RESEARCH BOT," and "CONFIDENCE BOOST!" with an encouraging smiley face. In the background, other students are also working on laptops. Image (and typos) generated by Nano Banana.
Research suggests a correlation between a lack of academic confidence in students and an increased likelihood of turning to AI tools for assistance. This image depicts a student utilising an AI interface offering “confidence boost” and “essay assist,” illustrating how AI can become a crutch for those feeling insecure about their abilities in the academic environment. Image (and typos) generated by Nano Banana.

Source

Inside Higher Ed

Summary

A survey by Inside Higher Ed and Generation Lab finds that 85 % of students claim they’ve used generative AI for coursework in the past year. Among the habits observed, students with lower self-perceived academic competence or low confidence are more likely to lean on AI tools, especially when unsure or reluctant to ask peers or instructors for help. The study distinguishes between instrumental help-seeking (clarification, explanations) and executive help-seeking (using AI to complete work). Students who trust AI more are also more likely to use it. The authors argue that universities need clearer AI policies and stronger support structures so that students don’t feel forced into overreliance.

Key Points

  • 85 % of surveyed students reported using generative AI for coursework in the past year.
  • Students with lower academic confidence or discomfort asking peers tend to rely more on AI.
  • AI use splits into two modes: instrumental (asking questions, clarifying) vs executive (using the AI to generate or complete work).
  • Trust in AI correlates with higher usage, even controlling for other variables.
  • Many students call for clear, standardised institutional policies on AI use to reduce ambiguity.

Keywords

URL

https://www.insidehighered.com/news/student-success/academic-life/2025/09/30/students-who-lack-academic-confidence-more-likely-use

Summary generated by ChatGPT 5