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This OER presents an updated Assessment Redesign Framework for higher education in the age of GenAI. It supports educators and programme teams in designing valid, transparent, and scalable assessments, integrating AI literacy, process-focused approaches, and guidance on AI detection, large cohorts, and emerging agentic AI challenges.
Purpose of the MTU Student Guidelines
Supports Academic Integrity Principles and MTU’s Academic Integrity Policy by:
1. Explaining what academic integrity is.
2. Helping students avoid bad decisions during assessments.
3. Outlines and signposts supports available across MTU
This resource showcases initiatives from both academic and professional support areas across DCU, which have been funded under SATLE – the Strategic Alignment of Teaching and Learning Enhancement Funding in Higher Education. Examples are provided under the themes of Education for Sustainable Development, Digital transformation & Academic Integrity
This Podcast Series has been developed by CPID Staff involved in teaching and engaging in educational research. It consists of podcasts with well-known educational academics and experts, exploring various educational concepts/topics taught on programmes offered by the CPID, and on SATLE funded educational research projects.
Academic Integrity Handbook for MTU staff.
Chapters:
1. Upholding Academic Integrity and Preventing Academic Misconduct
2. Detecting Academic Misconduct
3. Dealing with Academic Misconduct
This guideline document seeks to support the implementation of MTU policies and procedures which underpin Academic Integrity and help to address the implications of Generative Artificial Intelligence on assessment practices.
This study explores student perceptions and behaviours concerning academic integrity and the use of Generative AI in the context of Munster Technological University (MTU). It draws insights from a comprehensive survey of 608 students across various faculties and academic stages.
Reimagining Assessment & Feedback Together (RAFT) is MTU initiative developed to transforming assessment practices. This volume documents twenty-four situated, discipline-specific attempts to rethink assessment and feedback through student partnership and action research.
This opinion article outlines the alignment between patient-clinician consultations in healthcare and student-investigator meetings focused on academic misconduct
This paper entitled 'Developing an Academic Integrity Policy and Academic Misconduct Procedures in an Era of Generative Artificial Intelligence: Five Tips for Success' may be of interest to policy developers and educators in further and higher education and training organisations as they adapt to challenge of AI.
This document sets out a detailed, values-led framework to support the ethical adoption of generative artificial intelligence (gen AI) in teaching and learning across Irish higher education. It builds on the HEA Generative AI Policy Framework by translating high-level principles into concrete provisions to guide institutional policy, governance and educational practice.
The principles address five core areas: academic integrity, equity and inclusion, critical engagement and AI literacy, privacy and data governance, and sustainable pedagogy. Together, they provide institutions with a practical reference for navigating the ethical, pedagogical and organisational challenges associated with generative AI, while safeguarding academic standards, student rights and institutional autonomy.
This policy framework provides national guidance for the responsible and values-based use of generative artificial intelligence (gen AI) in teaching and learning within Irish higher education. It is designed to support educators, academic leaders and professional staff in making informed decisions about the adoption and integration of gen AI technologies in educational practice.
The framework focuses specifically on teaching and learning, addressing issues such as academic integrity, assessment design, equity and inclusion, AI literacy, privacy and data governance, and sustainable pedagogy. It sets out five core principles to guide institutional policy development and practice, while allowing for local adaptation and institutional autonomy.
This lesson is designed to aid students in understanding how to engage generative AI in an academic setting without breaching academic integrity policy.
This lesson is designed to teach students about academic integrity from understanding the principles and encouraging a perspective that they are equal partners in upholding academic integrity.
The Manifesto for Generative AI in Higher Education is a living resource for educators, students, and institutions. It invites reflection and dialogue across thirty statements exploring teaching, ethics, and imagination – helping higher education navigate AI with curiosity, integrity, and humanity.
This open resource presents over 140 real-world academic integrity and ethical dilemma scenarios co-created by students and staff. Designed for educators, trainers, and learners, it supports interactive discussions, gamified learning, and scenario-based teaching in ethics and integrity education across disciplines.
This resource presents AVINA, an automated visual novel generator using large language models to transform multiple-choice questions into interactive learning narratives. Designed for educators and students, it supports gamified training in academic integrity and ethical decision-making through adaptive storytelling and experiential learning.
Strategic Alignment of Teaching and Learning Enhancement Funding in Higher Education
The University of Limerick Academic Integrity and GenAI toolkit is a curated suite of resources developed to support ethical academic practices in the evolving landscape of Generative AI.
This GenAI Learning Hub was developed with students, for students, to support the responsible and effective use of generative AI. Topics are divided across three main sections to aid understanding of GenAI before use, during use, and in relation to assessment. While aimed at students, this resource will be useful to anyone using GenAI.
