This presentation introduces a model for student referencing of their use of AI sources in academic work. The TOPIC model - Tools, Outputs, Prompts, Iterations, Critical reflection - guides the referencing process for complex uses of generative AI tools, by specifying and rationalising the need to focus on structured specific components.

The presented work will include brief background on recent focus-group research with Irish third-level students, which suggests an urgent need for a rigorous deployable framework for students to consider and acknowledge their use of AI tools in academic assessment. The TOPIC model has been used successfully over the past year at DCU, and in linked ECIU universities outside Ireland, and provides a pedagogical value via its introduction - prompting thematic reflection and reporting on usage in addition to facilitating verification of relevant contextual use in evaluated work.

A briefing is provided on the use cases for the model, recent case studies of its utility in third level contexts, and examples of student work within it.

More on this model: https://doras.dcu.ie/30652/

Custom GPTs are emerging as transformative tools in education, with growing evidence of their potential to enhance teaching, learning, and research through intelligent, context-aware interaction (Aithal & Aithal, 2023; AACSB, 2024). However, their effectiveness is shaped by cultural, contextual, and ethical factors that influence learner engagement and perceived usefulness (Nyaaba & Zhai, 2024).

This small-scale study examines the use of a Custom GPT with Stage 2 Electrical Apprentices in Further Education and Training, evaluating its potential to support personalised learning. Drawing on survey responses and a short focus group interview, the study found that while all users reported improved understanding of key concepts, their overall experiences with the GPT varied widely. Views differed particularly on its effectiveness in identifying knowledge gaps, delivering personalised support, and providing human-like interaction.

Learners who experienced the GPT as more intelligent and human-like reported higher satisfaction. Responses ranged from finding the tool empowering and helpful to viewing it as lacking utility or personalisation. These findings suggest opportunities to improve onboarding, enhance GPT adaptiveness and interactivity, and clarify its role alongside traditional learning supports.

This presentation will describe how we developed a comprehensive Moodle maturity model designed to assist institutions in assessing their current use of Moodle in the institution, identifying areas for improvement, and charting a course towards enhanced Moodle maturity. Grounded in extensive research with focus groups involving several universities and aligned with JISC’s Digital maturity framework, the model encompasses key dimensions of Moodle maturity, including infrastructure, data utilisation, organisational culture, and knowledge development and knowledge exchange.

The model offers a structured framework of distinct maturity levels, each characterised by specific criteria and benchmarks. Institutions can leverage this framework to conduct self-assessments, benchmark against peers, and prioritise initiatives for improvement.

Key features of the LMS maturity model include:

1. Multi-dimensional Assessment: Evaluation across technology, data, people, processes, and strategy dimensions to provide a holistic view of digital maturity.

2. Maturity Level Definitions: Clear delineation of maturity levels with defined criteria, enabling organisations to gauge their current state and set achievable targets for progress.

3. Actionable Insights: Identification of actionable insights and recommendations tailored to each maturity level, facilitating targeted interventions and resource allocation.

This free tool is designed for Moodle but applicable to any LMS

Implementing a significant transition management programme to unify TU Dublin’s Virtual Learning Environments from 3 separate campus specific VLE’s to a single D2L Brightspace instance to achieve digital transformation and enhance the student experience.
TU Dublin’s oneVLE project was initiated to transition TU Dublin to a single D2L Brightspace instance.
TU Dublin previously operated and supported 3 VLE’s, 2 separate instances of Moodle and one Brightspace instance.
Despite now being part of the same Technological University, legacy practices and the geographic separation of the two campuses posed significant challenges.
By leveraging D2L HE Select Services and executing a comprehensive communications and training plan, we successfully overcame these challenges to unify our VLE in time for the 2024/25 academic year.