Artificial Intelligence (AI) is reshaping the landscape of higher education—particularly in science, technology, engineering, and mathematics (STEM) disciplines. With its capability to create interactive, engaging, and highly adaptable learning environments, AI holds the potential to revolutionize how engineering and computer science are taught. From intelligent tutoring systems to automated feedback and personalized learning pathways, AI introduces innovative tools that enhance both teaching practices and learning outcomes. However, this transformation also presents significant challenges. Concerns around academic integrity in assessments, the reliability and fairness of AI systems, data privacy, and the evolving role of educators must be carefully addressed. Beyond the classroom, AI is transforming the qualifications expected of new graduates. The growing digitization of industry and the rise of software-driven products are driving the demand for engineers and scientists equipped with both traditional technical skills and emerging digital capabilities. This shift calls for a fundamental rethinking—not just of how we teach, but also what we teach.
This workshop aims to bring together educators, researchers, AI developers, and institutional leaders to explore how we can harness the opportunities AI presents in engineering and computer science education at universities while proactively addressing the challenges. Through interdisciplinary dialogue and knowledge exchange, this workshop seeks to envision a future where AI complements human teaching and fosters more inclusive, impactful, and scalable learning environments for the next generation of engineers and computer scientists. It will also serve as a platform to discuss how education must evolve as AI reshapes engineering roles and workplace expectations. We invite submissions on a broad range of topics, including but not limited to:
- AI-Enhanced Teaching and Learning
- Design and evaluation of AI-driven tutoring and feedback systems
- Adaptive learning technologies and personalized learning pathways
- Use of large language models (LLMs) and generative AI in education
- Enhancing interactivity and student engagement through AI tools
- Virtual labs, simulations, and augmented learning environments
- AI-driven feedback and assessment mechanisms
- Academic integrity and the use of AI in student evaluation and examinations
- Ethical considerations: bias, fairness, and transparency in AI tools
- Data privacy and student agency in AI-supported learning environments
- Educator perspectives: roles, workflows, and training in the age of AI
- Case studies on integrating AI tools into courses and curricula
- The role of AI in collaborative and project-based learning
- Preparing educators to effectively use and supervise AI tools
- Ethical, legal, and societal implications of AI-enhanced teaching
- Student perceptions, learning outcomes, and motivation in AI-integrated settings
- Preparing Students for the New AI-Driven Workplace
- Curriculum reform to align with AI-related industry demands
- Integrating AI literacy and skills into engineering and computer science programs
- Cross-disciplinary approaches to teaching AI and digital skills
- Strategies for fostering critical thinking, adaptability, and lifelong learning
- University-industry collaborations to co-design relevant learning outcomes
- Credentialing, assessment, and recognition of emerging AI competencies
- Equity and inclusion in access to future-ready education
- Teaching coding and data skills to engineering students
The workshop will be held in conjunction with AIxSET 2025 in Laguna Hills, California, USA (hybrid format) on October 01 – 03, 2025. It solicits regular technical papers of up to 6 pages (IEEE double-column format). Workshop papers will be official publications of IEEE which will be included in IEEEXplore and also be available as printed workshop proceedings.