Generative AI

Embracing chatbots in higher education : the use of artificial intelligence in teaching, administration, and scholarship by Sidorkin, Alexander M. This book explores the integration of AI-powered chatbots such as ChatGPT into higher education for instructional and communication purposes. The author emphasizes the responsibility of higher education institutions to equip students with advanced skills for writing with AI assistance, and prepare them for an increasingly AI-driven world. Offering numerous practical tips, the book demonstrates how universities can increase student success, and stem the rising cost of higher education by employing AI tools. The chapters discuss streamlining tasks such as grading, providing feedback, and handling administrative duties, to show how educators can be enabled to focus on more meaningful aspects of their work. The author also reflects on the philosophical and ethical considerations and potential pitfalls of relying on AI in higher education, including concerns about academic integrity and the importance of human input in the learning process. The author offers a responsible and informed approach to incorporating the new powerful tools into the academic landscape. This volume will be a key resource for higher education faculty and administrators seeking to navigate the complex intersection of AI and writing.

AI Ethics in Higher Education: Insights from Africa and Beyond by Simon Atuah Asakipaam (Editor); Caitlin C. Corrigan (Editor); Jerry John Kponyo (Editor); Christoph Luetge (Editor) This open access book tackles the pressing problem of integrating concerns related to Artificial Intelligence (AI) ethics into higher education curriculums aimed at future AI developers in Africa and beyond. For doing so, it analyzes the present and future states of AI ethics education in local computer science and engineering programs. The authors share relevant best practices and use cases for teaching, develop answers to ongoing organizational challenges, and reflect on the practical implications of different theoretical approaches to AI ethics. The book is of great interest to faculty members, researchers, and students in the fields of artificial intelligence, computer science, mathematics, computer engineering, and related areas, as well as higher education administration.

Using Generative AI Effectively in Higher Education by Sue Beckingham (Editor); Jenny Lawrence (Editor); Stephen Powell (Editor); Peter Hartley (Editor) Using Generative AI Effectively in Higher Education explores how higher education providers can realise their role and responsibility in harnessing the power of generative artificial intelligence (GenAI) ethically and sustainably. This rich collection of established and evaluated practices from across global higher education offers a practical guide to leading an agile institutional response to emerging technologies, building critical digital literacy across an entire institution, and embedding the ethical and sustainable use of GenAI in teaching, learning, and assessment. Including reflections from stakeholders testifying to the value of the approaches outlined, the book examines how higher education can equip staff and students with the critical-digital literacy necessary to use GenAI in work, study, and social life responsibly and with integrity. It provides an evidence-based resource for any kind of higher education (HE) provider (modern, college-based, and research-focused) looking for inspiration and approaches which can build GenAI capability and includes chapters on the development of cross-institutional strategy, policies and processes, pedagogic practices, and critical-digital literacy. This resource will be invaluable to educational leaders, educational developers, learning developers, learning technologists, course administrators, quality assurance staff, and HE teachers wishing to embrace and adapt to a GenAI-enabled world.

How to teach responsible AI in Higher Education: challenges and opportunities by Aler Tubella, Andrea ; Mora-Cantallops, Marçal ; Nieves, Juan Carlos In recent years, the European Union has advanced towards responsible and sustainable Artificial Intelligence (AI) research, development and innovation. While the Ethics Guidelines for Trustworthy AI released in 2019 and the AI Act in 2021 set the starting point for a European Ethical AI, there are still several challenges to translate such advances into the public debate, education and practical learning. This paper contributes towards closing this gap by reviewing the approaches that can be found in the existing literature and by interviewing 11 experts across five countries to help define educational strategies, competencies and resources needed for the successful implementation of Trustworthy AI in Higher Education (HE) and to reach students from all disciplines. The findings are presented in the form of recommendations both for educators and policy incentives, translating the guidelines into HE teaching and practice, so that the next generation of young people can contribute to an ethical, safe and cutting-edge AI made in Europe.

Generative AI in Higher Education: Balancing Innovation and Integrity by Francis, Nigel J. ; Jones, Sue ; Smith, David P. Generative Artificial Intelligence (GenAI) is rapidly transforming the landscape of higher education, offering novel opportunities for personalised learning and innovative assessment methods. This paper explores the dual-edged nature of GenAI’s integration into educational practices, focusing on both its potential to enhance student engagement and learning outcomes and the significant challenges it poses to academic integrity and equity. Through a comprehensive review of current literature, we examine the implications of GenAI on assessment practices, highlighting the need for robust ethical frameworks to guide its use. Our analysis is framed within pedagogical theories, including social constructivism and competency-based learning, highlighting the importance of balancing human expertise and AI capabilities. We also address broader ethical concerns associated with GenAI, such as the risks of bias, the digital divide, and the environmental impact of AI technologies. This paper argues that while GenAI can provide substantial benefits in terms of automation and efficiency, its integration must be managed with care to avoid undermining the authenticity of student work and exacerbating existing inequalities. Finally, we propose a set of recommendations for educational institutions, including developing GenAI literacy programmes, revising assessment designs to incorporate critical thinking and creativity, and establishing transparent policies that ensure fairness and accountability in GenAI use. By fostering a responsible approach to GenAI, higher education can harness its potential while safeguarding the core values of academic integrity and inclusive education.

Generative AI in Higher Education: Teachers' and Students' Perspectives on Support, Replacement, and Digital Literacy by Haroud, Samia ; Saqri, Nadia Artificial intelligence (AI) is increasingly shaping diverse sectors, including education, sparking debates about its potential to transform pedagogical practices and redefine the role of educators. This study explores the perceptions and applications of generative AI in Moroccan higher education to better understand its implications for teaching and learning. A mixed-methods approach was adopted, combining quantitative data from 130 teachers and 156 students with qualitative insights. Quantitative findings reveal significant differences: students demonstrate greater openness to adopting AI, appreciating its capacity to provide instant feedback, enhance creativity, and improve academic performance. In contrast, teachers express reservations, particularly regarding AI's potential to undermine critical soft skills such as collaboration, problem-solving, and critical thinking. Qualitative analyses confirm these trends, highlighting that, while AI is perceived as a valuable complementary tool, it cannot replace the essential human role of educators in providing personalized guidance and addressing students' emotional and cognitive needs. Both groups agree on the necessity of enhanced digital literacy to ensure ethical and effective AI integration. These findings underscore the opportunities of generative AI, such as personalized learning and efficiency, while addressing limitations like ethical concerns and over-reliance, offering actionable insights for policymakers, educators, and technologists aiming to integrate AI responsibly in education.

Advancing AI in Higher Education: A Comparative Study of Large Language Model-Based Agents for Exam Question Generation, Improvement, and Evaluation by Nikolovski, Vlatko ; Trajanov, Dimitar ; Chorbev, Ivan The transformative capabilities of large language models (LLMs) are reshaping educational assessment and question design in higher education. This study proposes a systematic framework for leveraging LLMs to enhance question-centric tasks: aligning exam questions with course objectives, improving clarity and difficulty, and generating new items guided by learning goals. The research spans four university courses—two theory-focused and two application-focused—covering diverse cognitive levels according to Bloom’s taxonomy. A balanced dataset ensures representation of question categories and structures. Three LLM-based agents—VectorRAG, VectorGraphRAG, and a fine-tuned LLM—are developed and evaluated against a meta-evaluator, supervised by human experts, to assess alignment accuracy and explanation quality. Robust analytical methods, including mixed-effects modeling, yield actionable insights for integrating generative AI into university assessment processes. Beyond exam-specific applications, this methodology provides a foundational approach for the broader adoption of AI in post-secondary education, emphasizing fairness, contextual relevance, and collaboration. The findings offer a comprehensive framework for aligning AI-generated content with learning objectives, detailing effective integration strategies, and addressing challenges such as bias and contextual limitations. Overall, this work underscores the potential of generative AI to enhance educational assessment while identifying pathways for responsible implementation.