Conference Programme

Conference Programme*

*May be subject to minor amendments, up to date programme on conference website

09:25 - 09:55   Registration & Coffee

10:00 - 10:20   Welcome Remarks from Neil Gallagher, CCT College President,  Minister's Address and Launch of Conference, Minister for FHERIS - James Lawless TD*(*to be confirmed)

10:20 - 11:20   Keynote 1 - Dr. Carter Moulton From Values to Action: Human-Centered Education in the Age of AI

11:20 - 11:35   Coffee & Transition

11:35 - 12:20   Student Panel, chaired by Dr. Rebecca Roper

12:20 - 13:10  

Parallel Session 1 (Banking Hall, College Green Hotel)

Parallel Session 2 (Yeats Room 2.1, 2nd Floor, CCT College Dublin)

13:10 - 14:25   Lunch in Moreland's Grill - College Green Hotel

14:30 - 15:30   Keynote 2 - Rachel Koblic Teaching the Machine to Teach: Toward a Human-Centered, AI-Forward University

15:30 - 16:30   

Parallel Session 3 (Banking Hall, College Green Hotel)

Parallel Session 4 (Yeats Room 2.1, 2nd Floor, CCT College Dublin)

16:35 - 16:45   Final Reflections & Summary of Key Take-Aways

16:45 - 17:00   Conference Closing & Wrap Up

From Values to Action: Human-Centered Education in the Age of AI

Problematizing the dominant marketing narratives about AI's "speed," "efficiency," "scale," and "productivity," this interactive keynote reframes generative AI's impact on education around the human values, skills, and concerns that matter most to us as educators. What happens when we approach generative AI's emergence using a different set of terms—concepts like "community," "trust," "patience," and "discernment"? Drawing on Analog Inspiration, a card deck project featuring over 80 concepts ranging from "accessibility" to "wonder," this session introduces and explores the notion of human-centered AI pedagogy. Using human values, skills, and concerns as a lens, attendees will acquire practical strategies for cultivating trust with students, making student thinking visible, fostering intrinsic motivation, and strengthening the human relationships that anchor the work of teaching and learning. Participants will have a chance to engage with the Analog Inspiration cards during the session, and will leave with concrete teaching strategies they can implement in their own classrooms. 


Teaching the Machine to Teach: Toward a Human-Centered, AI-Forward University

Generative AI can answer almost any question, but answering is not the same as teaching. This session looks at what it takes to design AI that genuinely supports how people learn, and how careful design of the systems around a model keeps human judgment and relationships at the center. Rachel Koblic draws on hands-on work building learning systems to offer a grounded picture of what a human-centered, AI-forward university could be.

Presentation 1:.

Responding to GenAI in Health Professions Education: A Design Thinking Approach to UDL-Informed Assessment Redesign 

Dara Cassidy and Jenny Moffet, RCSI University

This case study focuses on the redevelopment of assessments within the Postgraduate Diploma in Health Professions Education, an online teaching qualification offered to RCSI staff and external students who wish to develop skills in educational practice. An increasing number of students were engaging in unauthorised generative AI (GenAI) use to complete assessments. In response, staff and student feedback, gathered via an online survey, informed a series of design thinking workshops aimed at redeveloping assessments aligned with Universal Design for Learning principles whilst supporting authentic learning alongside GenAI use. In 2025–26, new group project and flipped classroom activities were introduced, receiving improved student evaluation ratings. An updated reflective portfolio will be trialled in 2026–27, capturing students' educator identity development through real-world community of practice activities, such blogging, podcasting, or journal club participation. Consistent with UDL principles, students may choose formats that suit them, provided they meet the brief and learning outcomes. Reflection is a key element of higher education and a vital lifelong learning skill. Rather than retreating from it under GenAI pressures, programmes should educate students on the value and purpose of authentic reflection, supported by clear guidance on differentiating between GenAI uses that support or undermine learning. The portfolio explicitly foregrounds authentic expression, embedded in the learning outcomes. An introductory class addresses acceptable GenAI use, and programme governance includes a viva provision in cases where academic integrity issues have arisen.

Presentation 2: 

Teaching Responsible AI Through Assessment: Evidence from a Postgraduate Digital Marketing Course 

Noreen Henry, ATU

Institutional Context

The work took place within the MSc in Digital Media & Marketing programme at ATU, a postgraduate programme preparing learners for leadership roles in digitally transforming organisations. The assessment was delivered in person to a full-time cohort and was developed in response to the increasing influence of AI on marketing practice, while also reflecting institutional commitments to academic integrity, ethical AI use, and authentic assessment design.

What Was Attempted

A project-based assessment was designed in which students identified a real marketing problem, developed a proof-of-concept AI automation or agent, and produced an accompanying strategic implementation plan. Students delivered an 8-10 minute live demonstration and completed a report requiring critical analysis of use cases, implementation planning, governance considerations, and recommendations for organisational adoption. The intention was to connect digital transformation theory with practical experimentation while encouraging students to evaluate AI as a strategic business capability rather than a standalone technology.

What Happened

Students produced a range of innovative solutions addressing content creation, customer engagement, workflow automation, and decision support. The live demonstrations increased engagement and provided opportunities for peer learning, while the implementation planning element encouraged deeper consideration of organisational readiness and change management. A recurring challenge was that some students initially prioritised technical functionality over business justification and governance requirements. However, the strongest submissions demonstrated a balanced understanding of technology, strategy, ethics, and implementation.

What is Means

The experience demonstrates that AI assessments can support higher-order learning when they require students to connect experimentation with critical evaluation and strategic decision-making. Future iterations will place greater emphasis on stakeholder analysis, implementation metrics, and evidence of business impact. For educators, the key lesson is that authentic AI assessment should assess judgement, governance, and organisational application rather than technical outputs alone.

Academic Integrity Considerations

Academic integrity was embedded through explicit guidance requiring transparency about generative AI use, verification of sources, critical review of AI-generated content, and clear distinction between AI assistance and original student analysis. This approach positioned responsible AI use as part of the learning process rather than something to be avoided.

Presentation 1:.

From Awareness to Practice: Building Critical GenAI Literacy Through Formative and Process-Oriented Authentic Assessments 

Dr. Muhammad Iqbal, CCT College

The rapid adoption of generative artificial intelligence (GenAI) in higher education presents significant challenges and opportunities for learning support while introducing pedagogical, epistemic and ethical challenges (Kasneci et al., 2023). Large language models (LLM) generate responses using a non-deterministic (probabilistic) approach, meaning that identical prompts may produce different outputs, including information that is inaccurate, fabricated or biased (Bender et al., 2021). The objective of this study is to raise student awareness of these GenAI characteristics and support the translation of this awareness into practice, especially in contexts where accuracy, verification, and reproducibility are core requirements in computing.

To address these challenges, a teaching approach integrating critical GenAI literacy, lecture-linked formative quizzes and process-oriented authentic assessment was implemented across modules at CCT College Dublin during 2025–2026. Students were introduced to the capabilities and limitations of GenAI through practical classroom activities. For example, students submitted similar prompts to a GenAI tool and compared the resulting responses with peers. Observing variation, diverse responses and inconsistencies first-hand helped them to illustrate why GenAI-generated content should be critically evaluated rather than treated as authoritative.

Formative Moodle quizzes were introduced to engage with lecture material and aligned with module learning outcomes, assessing understanding, application and enhancing problem solving skills. Time constraints and restrictions on copying and pasting were used to encourage active engagement with the material during quizzes. Students also received clear guidance on acceptable and unacceptable use of GenAI and the limited assistance was permitted if declared, while the disciplinary content and reasoning remained the student's own — building on an existing academic-integrity framework at CCT College Dublin (Iqbal and McQuaid, 2025).

Process-oriented authentic assessment was incorporated by requiring students to provide evidence of their work and development process through reports or posters, screenshots, video demonstrations, artefacts, and version-control histories to be triangulated against demonstrated understanding across QQI levels 7–9. These processes helped lecturers to identify each student's specific contribution, effort, and in-depth understanding, along with a time-stamped record of their engagement, such as GitHub, a requirement for supporting deep learning. The resulting framework integrates critical GenAI literacy, formative concept-checking, and authentic process-oriented assessment practices to promote responsible AI usage while prioritising demonstrable learning processes over reliance on generated outputs.

References

Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J. and Kasneci, G. (2023) 'ChatGPT for good? On opportunities and challenges of large language models for education', Learning and Individual Differences, 103, 102274.

Bender, E.M., Gebru, T., McMillan-Major, A. and Shmitchell, S. (2021) 'On the dangers of stochastic parrots: Can language models be too big?', in Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21). New York: Association for Computing Machinery, pp. 610–623.

Iqbal, M. and McQuaid, D. (2025) 'Adaptive framework for enhancing academic integrity literacy and ethical use of generative AI tools', in Enhancing Academic Integrity: From Ideas to Action. Conference Proceedings, April, ICT Faculty, CCT College Dublin, p. 30. Available at: https://arc.cct.ie/staff_publications (Accessed: 3 October 2025).

Presentation 2: 

Embedding AI literacy in New Programme Development in Emerging GenAI Era 

Atif Atif, Griffith College

Griffith College is a private higher education institute with campuses in Dublin, Cork and Limerick. This work was undertaken at the Limerick campus as part of the development and QQI validation of a new MSc Business Analytics (BA) programme.

I led an industry-informed development of the proposed MSc programme. Programme development slowed because of other academic responsibilities. When it was reviewed again a few months later, the industry needs had changed quickly. Employers now expect BA graduates to have skills in AI incorporated in all required tools (programming, data management, data visualisation, data governance and ethics). Industry is seeking graduates who could combine technical analytics skills with an understanding of how AI support data-driven decision making. Moreover, some early module content and assessment approaches had been developed using AI without consideration of academic integrity.

The revised programme incorporated an AI for BA core required skills. Assessment approaches were also revised to reduce AI vulnerability and newly appointed module leaders were supported through a workshop on writing modules and assessments in an ethical way, including how to use AI responsibly.

The experience showed how quickly programme development can become outdated when industry and technology are evolving simultaneously. This also led to discussion about adding AI to the programme title in order to attract students and employers. The team recognised that academic staff need to use AI responsibly when developing curriculum and assessment materials, while students need to understand appropriate AI use, its limitations and their responsibility for the accuracy of submitted work.

Presentation 1:.

GenAI as a Disorienting Dilemma: Reframing Academic Integrity through student partnership 

Silvia Benini, Mary-Clare Kennedy, Suzanne Stone, David F. Moloney and Mary Fitzpatrick, University of Limerick

The introduction of Generative AI (GenAI) into education has intensified the volatility, uncertainty, complexity, and ambiguity (VUCA) already shaping contemporary learning environments. While the VUCA framework helps describe this external instability (Chan et al., 2025), GenAI also challenges deeper assumptions about authorship, knowledge production, assessment, and the role of educators. It should therefore be understood not merely as another educational technology to be incorporated into existing practice, but as a destabilizing element that requires educators and learners to reconsider the foundations of teaching, learning and assessment practices. Recent research (Benini & Murray, forthcoming) positions GenAI as a disorienting dilemma within Mezirow’s transformative learning theory (Mezirow, 1991). Although VUCA describes the external condition of instability, Mezirow’s concept of the disorienting dilemma helps to explain the internal cognitive, ethical, and professional responses that GenAI provokes. In this context, rather than treating GenAI solely as a challenge to be managed, it is essential to frame it as a supportive tool that enhances, rather than replaces, human judgement, expertise, and creativity.

An example of this theoretical approach is illustrated through a case study conducted at the University of Limerick which examines student partnership in the development of resources designed to support the understanding of GenAI and academic integrity in higher education. The case study offers insights into how student partners can contribute to the creation of relevant, accessible, and context-sensitive guidance while also providing perspectives on GenAI, academic integrity, and their evolving relationship within contemporary higher education. 

Presentation 2: 

From Policy to Practice: Building Student AI Literacy Through the AI Assessment Scale at TU Dublin 

Bhuvan Israni, TU Dublin

Generative Artificial Intelligence (GenAI) is rapidly changing how students learn, complete assignments and demonstrate achievement in higher education. For lecturers, communicating appropriate AI use can be challenging when expectations differ across modules and assessments. This case study presents a practice-based approach at Technological University Dublin (TU Dublin), translating institutional GenAI guidance into a practical assessment intervention within a Level 8 Corporate Social Responsibility module.

Institutional Context

TU Dublin has developed Guidelines on the Responsible Use of Generative AI, including the Artificial Intelligence Assessment Scale (AIAS), to support transparent and assessment-specific decisions about GenAI use. The five-level scale ranges from Level 1 (no AI) through brainstorming, editing and AI-supported task completion to Level 5 (full AI co-pilot use). Within this context, the CSR module provided an opportunity to translate institutional guidance into a practical student-facing approach to AI literacy.

What Was Attempted

I developed a student-facing learning activity based on the five levels of the AIAS, ranging from no AI use to full AI co-pilot use. Students examine different assessment scenarios and determine what level of GenAI use is appropriate, what forms of use are permitted, what should be disclosed, and where independent judgement remains essential. The approach was designed to move students beyond simply knowing the institutional policy towards applying it to their own assessment decisions.

What Happened

The approach created a structured way for students to engage with GenAI as an assessment and learning issue rather than viewing it simply as permitted or prohibited. Students were encouraged to consider the purpose, extent and responsibility associated with different forms of AI use. The activity also made the distinction between using GenAI and critically evaluating GenAI more visible within the learning process.

What It Means

The experience suggests that institutional GenAI policy becomes more meaningful when translated into concrete assessment decisions that students can practise and apply. A key takeaway for colleagues is that AI literacy can be embedded within existing curriculum and assessment practices rather than treated as a separate digital-skills activity.

Academic Integrity Considerations

Academic integrity was embedded through explicit expectations around transparency, disclosure, critical evaluation and student accountability. The approach positions responsible GenAI use as part of assessment literacy, rather than relying solely on restrictions or detection.

Presentation 3: 

From Framework to Infrastructure: Embedding the 4D AI Fluency Model in Assessment Practice at CCT College Dublin

Fiona O’Riordan, Triona Kearns, and Ruth Ní Bheoláin, CCT College

Generative AI's ubiquity has outpaced some assessment infrastructure, at times leaving policy and practice disconnected. This case study describes CCT College Dublin's response: the institution-wide integration of the 4D Artificial Intelligence (AI) Fluency Framework (Delegation, Description, Discernment, Diligence; Dakan, Feller and Anthropic, 2025) into assessment briefs, cover sheets, and Moodle declarations.

The underlying institutional stance is that AI use should be assumed rather than prohibited for non-secure assessments; the challenge has been building infrastructure for transparent declaration and evidencing rather than policing. With Executive Leadership Team approval in principle, the framework moved through structured consultation with lecturers and programme leads, surfacing genuine tensions: how to weight non-completion of AI declarations without defaulting to misconduct referral, how to keep declaration proportionate to existing academic integrity requirements, and how to give lecturers specific rather than generic guidance on permitted use.

This presentation will reflect honestly on what that consultation process revealed about institutional readiness — where top-down policy direction met bottom-up practice, and how a scaffolded, consultative approach helped secure buy-in across programmes. Central to the framework is a requirement for students themselves to submit a reflexive, critical reflection on their own AI engagement — using the AI Fluency Framework to evaluate the judgement, discernment and verification they applied, rather than simply declaring that AI was used. It will share CCT's experience of what supports and resources they are developing for both students and lecturers, to help embed the AI Fluency Framework in their assessments. The aim is that each AI declaration can be embedded coherently in every assessment, rather than bolted on, thus developing students and staff AI literacy and enhancing academic integrity.

Presentation 1:.

Artificial Intelligence in Teaching, Learning and Assessment: An AI Fluency Case Study Across Business and Computing Faculties 

Triona Kearns, Fiona O'Riordan, Fawzi Abusalama, Taufique Ahmed, Denis Cummins, Muhammad Iqbal, Matt Lemon, David McQuaid, Ruth Ní Bheoláin, Michael Weiss, Sam Weiss. CCT College

This presents an institutional case study of Generative AI (GenAI) use in teaching, learning and assessment (TLA) across CCT College Dublin's business and computing faculties. Rather than describing practice as an undifferentiated list of tools, the study applies the Framework for AI Fluency (Dakan & Feller, 2025) — Delegation, Description, Discernment and Diligence (“the 4 Ds”) — as an analytic lens to nineteen staff-submitted case studies, revealing patterns that discipline-by-discipline anecdote alone would miss. Delegation dominates the dataset but is largely shallow, first-draft use; Description and Discernment, though rarer, are qualitatively richer; and Diligence — ethical and academic-integrity responsibility — is conspicuously absent even from the most sophisticated cases, including one generating attack-style cybersecurity material for teaching. This “Diligence gap” is not an abstract concern but a live institutional one, directly informing CCT's redesigned Assessment Brief Template, which now embeds all four competencies into a mandatory “AI Declaration table” based on AI Fluency Framework, giving both staff and students a shared, documented structure for declaring and justifying AI use rather than a binary permitted/not-permitted clause. Thus the work speaks to authentic, real-world practice change; to ethical use of AI as a designed-in requirement rather than an afterthought; and to student partnership, since students now complete their own reflection against the same framework. It offers both a worked example of applying the AI Fluency Framework to a TLA setting and a concrete governance artefact already shaping institutional response.

Presentation 2: 

Exploring the space between AI and inclusion: A policy analysis of the HEA Gen AI policy. 

Trevor Boland, DCU

The presence of AI has advanced the scope of Assistive Technology (AT) to support students with disabilities in Higher Education. In my AT practice one AT in particular supports a wide range of disabilities in regards note taking and recording audio in class. Recording at times may be challenging to facilitate but the fears and unknowns that Gen AI is bringing to the section is causing AI resistance and concerns of various types. This resistance can be seen with the AI overlap with AT and now AT which should be supporting inclusion can now be hampered by fears around AT for studies. This raises concerns about the advancement of inclusive education as it harnesses AI and the institutions and staffs knowledge and awareness of AI when used with AT.

Presentation 3: 

The Gen AI Assessment Design Sprint: Using Prompt-based Scaffolding to align Pedagogy, Policy, and Practice. 

Dr Derek Dodd and Dr Ana Schalk  

This presentation shares a learning activity developed for lecturers undertaking TU Dublin's Microcredential in Assessment and Feedback: the GenAI Assessment Design Sprint. The Sprint is a structured, self-directed activity in which participants work through a sequence of scaffolded prompts in Microsoft Copilot Chat, supported by a grounding document covering institutional guidance, pedagogical principles, ethical considerations, and the AI Assessment Scale (AIAS). 

Rather than asking Copilot to redesign assessments, the prompt sequence positions it as a pedagogical sounding board. Lecturers critically review an existing assessment, identify vulnerabilities to inappropriate AI use, determine an appropriate level of permitted AI engagement, develop a layered assessment strategy, and produce clear guidance for students, documenting their reflections and evolving thinking, and critically evaluating Copilot's responses throughout. 

Drawing on participant feedback and our experience of designing and administering the sprint, this presentation considers the extent to which the design sprint may have supported lecturers in engaging more critically with assessment design in an AI-enabled context.  Rather than evaluating the effectiveness of the intervention, it explores how participants described their experience of using scaffolded prompting to make pedagogical reasoning more explicit, surface assumptions about GenAI, and reflect on tensions between authenticity, academic integrity, and student learning. The reflections also raise questions about the role and limitations of GenAI as a partner in assessment design, and the continuing importance of educator judgement in interpreting, challenging, and adapting AI-generated suggestions.