Generative Artificial Intelligence in Higher Education

Permanent URI for this collectionhttps://hdl.handle.net/10125/112523

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Highlighting AI Competencies: A Qualitative Analysis Approach Using AI Agents
(2026-01-06) Elnoshokaty, Ahmed; El-Gayar, Omar; Wahbeh, Abdullah; Al-Ramahi, Mohammad; Nasralah, Tareq
The rapid advancement of Artificial Intelligence (AI) is reshaping the job market, creating a demand for new skills and competencies in AI and machine learning (ML). As academic institutions strive to align educational programs with industry needs, a clear understanding of job market expectations becomes essential. This study employs a qualitative, grounded theory approach, enhanced by large language models (LLMs), to analyze job postings related to AI. Using a custom coding framework and AI agent arbitration, we extract and classify the competencies sought by employers into eight major categories. Key findings emphasize the significance of technical skills, including system analysis, architecture, programming languages, and machine learning, as well as soft skills such as collaboration and leadership. The results offer critical insights for curriculum developers and accreditation bodies, providing a scalable, auditable methodology for aligning academic programs with real-world AI job market demands.
Autonomous or Automated? Chatbot-Mediated Self-Directed Learning among Undergraduate Computing Students
(2026-01-06) Schwertfeger, Sharmen; Kesavamoorthy Vijayalakshmi, Sumesh; Williams, Jason
This qualitative study examines how Artificial Intelligence (AI) chatbots mediate self-directed learning (SDL) among undergraduate computing students. While chatbots served dual roles as instrumental tools and affective supports, and students verified outputs using critical strategies, we identified emergent default-use patterns. These patterns, characterized by static prompting and reliance on chatbots as a primary resource, limited strategic flexibility and resource diversity. Our findings advance SDL theory by demonstrating how chatbot mediation can simultaneously foster and constrain learner autonomy.
Integrating an Intelligent Tutoring Chatbot into Higher Education: The Case of Teaching Declarative Process Modeling through Generative AI
(2026-01-06) Nagel, Sabine; Wolters, Anna; Riehle, Dennis M.; Delfmann, Patrick
This paper presents a case study on the classroom use of an intelligent tutoring chatbot designed to support teaching Declarative Process Modeling within a master’s-level course. Replacing traditional lectures, the chatbot delivered interactive, task-based modules with real-time feedback and conversational support. Across three design iterations, we collected and analyzed student feedback, system usage data, and survey responses to examine how learners engaged with the chatbot, what challenges they encountered, and how their input informed system refinement. Students used the chatbot to self-regulate their learning, frequently asked clarifying questions, and responded critically to feedback. Their contributions shaped improvements in instructional clarity and usability. The findings demonstrate the potential of Generative AI to enable adaptive, student-centered learning experiences in higher education when thoughtfully integrated into course design.
Co-Piloting with GenAI: A Functional Typology of Student-AI Interaction in Business Education
(2026-01-06) Bass, Erin; Elson, Joel; Pleggenkuhle-Miles, Erin
To prepare business students for an AI-enabled workforce, educators are increasingly integrating generative AI (GenAI) tools into the classroom. Yet little is known about how students actually use these tools for complex tasks. This study examines how undergraduate students use GenAI during a strategic decision-making activity. We analyzed 167 student prompts using a dual-framework approach: the AI-ICE model to assess cognitive engagement, and an inductively developed typology of GenAI co-pilot roles: Content Generator, Task Executor, Advisor, Thinking Partner, and Role-Shifting. While students demonstrated cognitive range, most used GenAI in limited functional roles. Even when tasks called for strategic thinking, students rarely used GenAI as a collaborator. This disconnect between what students think and how they use GenAI highlights a gap in current instructional practice. Our findings offer a functional typology of student-GenAI interaction and practical insights for designing GenAI-enabled learning experiences in business education.
From Lecturers to Learning Architects: Rethinking Educator Roles in the Age of AI
(2026-01-06) Stöber, Anna-Luisa; Friese, Astrid
Artificial Intelligence (AI) is rapidly transforming education, necessitating a reevaluation of educators' roles and curriculum design. This study explores university educators' attitudes, usage patterns, and preferences regarding AI integration in teaching and learning. Using a mixed-methods approach combining focus groups and a quantitative survey at a German University of Applied Sciences, we identify both opportunities and concerns. Findings show that while educators generally welcome AI’s potential, especially for enhancing interactivity, personalizing learning, and reducing administrative tasks, there is a strong need for formalized training, ethical guidelines, and increased digital literacy. Digital literacy emerges as a key mediating factor influencing AI adoption, particularly in terms of perceived usefulness and ease of use. Educators are poised to evolve from content deliverers to strategic mentors, emphasizing critical thinking and creativity. However, barriers such as academic dishonesty, reliability concerns, and insufficient institutional support remain. The study calls for systemic strategies to build digital competence and enable sustainable AI integration.
From Syllabus to Assignment Design: A Case Study of GenAI Future Role in University Assessment
(2026-01-06) Gallo, Vincenzo; Pietrosanto, Antonio; Liguori, Consolatina; Paciello, Vincenzo; De Santo, Massimo
The growing integration of generative language models in higher education has prompted renewed attention to their role in supporting instructional design, particularly in developing assessments. This study explores the potential of such tools to assist in creating structured assignments within a university-level STEM curriculum. A systematic methodology was applied to evaluate outputs across key pedagogical dimensions, including alignment with learning objectives, appropriateness of difficulty, and cognitive depth. While the tools effectively generated technically accurate and syllabus-aligned content, persistent limitations were identified in their ability to produce higher-order reasoning tasks and multi-layered assessment items. These constraints were especially evident in advanced or design-based coursework. The findings suggest that generative models can enhance instructional efficiency and provide a valuable starting point for educators, but their outputs require ongoing refinement and professional oversight. Their optimal use lies in supporting, not replacing, the instructor, enhancing pedagogical expertise in meaningful assessment.
Empowering International Students’ Listening and Speaking Skills via AI-Driven Audio in NotebookLM: Reflections Across Disciplines
(2026-01-06) Zhao, Xin; Chen, Xuanning; Carratù, Marco; Shallari, Irida; Rollins, Minna
Despite the growing interest in GenAI, there has been limited focus on its multimodal potential, moving beyond text-based applications to auditory learning. One promising avenue is using AI-generated podcasts based on self-selected academic literature, enabling learners to engage with content in new ways. Research suggests that students, especially those who are not native English speakers, often struggle with academic speaking skills. This paper explores using NotebookLM. Learners can listen to a podcast generated and practice discussions with the AI-hosts to deepen their understanding of key concepts and prepare for live seminars. Drawing on pedagogical reflections from teachers of international students, this paper explores how this technology can support diverse learners in engaging with academic literature and developing academic speaking skills, as well as the potential concerns associated with its use. The paper concludes with recommendations for educators on better supporting international students through multimodal, AI-enhanced learning tools.
Introduction to the Minitrack on Generative Artificial Intelligence in Higher Education
(2026-01-06) Shallari, Irida; Rollins, Minna; Zhao, Xin; Carratù, Marco

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