Abstract:

The professional development of university teachers is a prerequisite for designing educational processes grounded in active, student-centred methodologies. Within this institutional vision, Teaching and Learning Centers (TLCs) play a strategic role by mapping needs, systematizing initiatives, and aligning training with quality assurance and curriculum innovation. This paper presents a multi-stage professional development programme run by the Teaching and Learning Center (TLC–CIMDU) at the University of Palermo to support academic staff in understanding, integrating, and critically evaluating generative artificial intelligence (AI) – including tools such as ChatGPT, Copilot and Gemini – in higher education.

The programme is structured around three strands:
(1) conceptual foundations (AI ethics, academic integrity, assessment validity, accessibility and inclusion);
(2) design laboratories (backward design for AI-aware syllabi, prompts and tasks for critical and creative thinking, redesign of formative and summative assessment, rubrics and transparency); and
(3) practice and reflection (micro-teaching, classroom simulations, peer observation, and reflective journals). Each cohort follows a learning pathway that blends workshops, online resources, mentoring, and participation in a community of practice moderated by the TLC.

A mixed-methods evaluation was conducted using questionnaires, reflective journals, and focus groups involving both teachers and students. Results reveal distinct perceptions: many teachers report caution or scepticism about integrating AI into their teaching, expressing concerns about workload, reliability, and fairness; students, instead, show greater readiness and expect explicit guidance on ethical and effective AI use. Training activities helped reduce uncertainty, increase self-efficacy, and foster a principled stance: participants reported improved confidence, creativity, and ethical awareness; they also developed the ability to design AI-resilient tasks, promote academic honesty, and leverage AI for feedback, scaffolding, and accessibility.

A key institutional outcome is the development of University Guidelines for the use of AI in didactics, defining shared principles, roles, and responsibilities for both teachers and students, clarifying assessment policies, and providing examples of AI-enhanced activities. The experience underscores the centrality of TLCs in orchestrating coherent, scalable interventions that translate institutional values into teaching practice, ultimately enabling a human-centred, ethical, and effective integration of AI in higher education.

Keywords:

Teacher training, Artificial Intelligence, Higher Education, Didactics Innovation, Digital Competence, Student-centred Learning.