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.

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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.

 

Topics of Interest

​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

 

Contributions will be reviewed for quality and relevance to the workshop’s theme. Theoretical and applied papers and papers that capture best practices and lessons
learned from field studies are encouraged. Submission of preliminary results would also
be considered.

Submissions in PDF format through this link by August 20, 2025.

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IMPORTANT SUBMISSION INSTRUCTION:

• When submitting a paper to the TECS-AI Workshop, please choose the "Workshop on Transforming Engineering and Computer Science in the Age of AI" track in Easychair.

• Page limits:

  • Regular and Workshop Papers – 8 pages (up to 2 extra pages allowed at $150 per extra page)

  • Short Papers – 4 pages (1 extra page allowed at $150)

  • Position Papers – 2 pages (1 extra page allowed at $150) 

 

The workshop will begin with a series of paper presentations. Depending on the number
of paper submissions and time available, an open discussion that invites active participation from the audience will conclude the workshop. This part of the workshop
would include an informal panel discussion to spark dialogue ensuring that participants benefit from expert insights while also creating space for diverse voices. Many valuable perspectives—especially those concerning how curricula should evolve to equip students with essential AI and digital skills—may not fit neatly into traditional academic papers. By combining structured presentations with audience-driven discussion the participants will benefit from the workshop. In short, the workshop aims to foster collaborative thinking on how and what we teach in an era of rapid technological change.

 

For more information about the TECS-AI Workshop, please go to https://ieee-tecs-ai.org.

Workshop Organizers

Maren Schnieder,* Hochschule Bochum University of Applied Sciences, Germany
Florian Schimanke, Hochschule Weserbergland, Germany
Jay Lofstead, Sandia National Laboratories, USA

Gary Glesener, Access Physical Models, LLC, USA

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* Corresponding Workshop Organizer

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