Tutors AI is GenAI augmentation into Tutors which enhances students' learning experience and empowers content creators to adjust content based on demand.

Tutors is a learning platform used by SETU students to access lectures and labs. It provides transformative learning experiences using open web standards. Tutors AI project facilitates diverse academic needs and learning styles by embedding several GenAI features into the Tutors app, such as "Chat with Tutors AI", "Explain Like I'm Five" and "AI Powered Web Search". It also includes "Tutors AI for Content Creators", a feature for lecturers which highlights areas where students need more support and provides insights for further content improvement.

The inspiration for the project came from Dr. Barbara Oakley’s “Learn how to learn” course, where she recommends using analogies to improve understanding of complex systems.

Tutors AI will help students to find answers to challenging questions and concepts within their online learning environment in real-time, encouraging deeper engagement with course materials. Data is available to adapt content to student needs. Ultimately it will minimise the time lecturers spend explaining basic concepts, allowing them to dedicate more attention to addressing advanced students’ questions.

This presentation discusses the process of developing institutional level analytics and data presentation facilities. Funded by the Technological Sector Advancement Fund (TSAF), the authors have been leading a team creating learning analytics to improve the student experience by optimising student support channels and creating learning analytics to support the student journey and allow the university to provide early interventions in a timely fashion where required.

This project brings together technical, administrative and academic aspects to examine what is required of an institutional analytics system to best serve all stakeholders within the University. The project, building on previous work, has shown that technically, many exciting possibilities exist, some driven by machine learning, for identifying students who would benefit from timely intervention. The presentation shows how Microsoft Fabric can be used to ingest, transform and serve data from a wide variety of sources to the chosen audience.

The ability to present this information raises many practical and ethical questions about what to do. This leads us to ask, now that we can present our data in such a powerful way, what do we do next - whose job is it anyway?

Despite a decade of promises around educational predictive analytics, from identifying at-risk students to providing personalised feedback, the practical impact of such systems remains marginal in many institutions. Drawing on experience as both an IT professional and academic working with enterprise educational platforms, this paper examines whether Large Language Models (LLMs) will meaningfully transform this landscape or merely reframe existing challenges.

Rather than focusing on theoretical capabilities, this analysis considers what AI can realistically deliver within the constraints of complex institutional systems, limited educator time, and competing pedagogical priorities. Even sophisticated AI provides minimal value when it either confirms what educators already know or identifies problems that cannot be practically addressed.

The current moment presents unprecedented opportunities to use powerful analytical and automation tools. However, the "not-yetness" of these initiatives often stems from unclear objectives and implementation barriers. This paper outlines common challenges educational institutions face when adopting AI, identifies necessary practical steps for successful implementation, and establishes realistic expectations for impact. By bridging technological possibilities with institutional realities, we can move beyond hype to develop AI applications that genuinely enhance educational outcomes in sustainable, meaningful ways.

Throughout the development and implementation of ambitious, yet financially and temporally finite research projects, the question of legacy is ever-present. How can the achievements and ethos be sustained once the initiative itself has concluded?

The National Technological University Transformation for Recovery & Resilience (N-TUTORR) was an innovative partnership across the entire Irish Technological Higher Education sector. Funded by the HEA, this project sought to embody a collaborative approach whereby colleagues across the Technological University sector could transform learning, teaching & assessment through the promotion and integration of 6 key themes:

• Sustainability and Education for Sustainable Development

• Digital Transformation

• Employability

• Equity, Diversity and Inclusion

• Universal Design for Learning

• Academic Integrity

The N-TUTORR Curriculum Framework is a defined, supportive pathway for all staff engaged in programme and module development. Through a legible collection of workshops and resources staff are guided through each stage of the programme development process. The Curriculum Framework curates knowledge and expertise from across the university, aligning it with policy and procedure to ensure clarity and accessibility.

In this way the framework promotes consideration of the themes mentioned above from the earliest stages of the design process.

This presentation will detail the concepts underlying the creation of the Curriculum Framework, the process through which the initial draft was designed and ongoing plans for future development, review and implementation.