Artificial Intelligence (AI) is reshaping the landscape of science, technology,
engineering, and mathematics (STEM) disciplines.

The workshop welcomes papers addressing (i) new AI enabled workflows within the
STEM disciplines – in particular engineering and computer science – as well as (ii)
the resulting modifications in the curriculum of degree programs and (iii) the way
these disciplines are taught at Universities.

From natural language user interfaces, that bridge humans and engineering
software, to intelligent AI systems capable of data analysing, generative designing,
operational decision‑making, quality assurance and predictive maintenance, large
language models and AI tools are transforming STEM workflows.

Beyond the classroom itself, 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.

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. However, this transformation also presents significant challenges
around, for example academic integrity in assessments, the reliability and fairness of
AI systems.

This workshop aims to bring together educators, researchers, AI developers, and
institutional leaders to explore how we can harness the opportunities AI presents in
typical engineering and computer science workflows while proactively addressing the
challenges. Through interdisciplinary dialogue and knowledge exchange, this
workshop seeks to envision a future where AI complements human capabilities within
STEM disciplines. The workshop 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-Enabled Workflows in STEM

• Integration of AI and large language models (LLMs) into engineering and computing workflows

• AI-assisted code generation, debugging, documentation, and software maintenance

• Generative design and optimization tools powered by machine learning

• Predictive maintenance, fault diagnosis, and intelligent monitoring systems

• AI-supported modelling, simulation calibration, and data-driven parameter tuning

• AI for quality assurance, anomaly detection, and materials inspection

• Data analytics and decision-support systems using AI for complex engineering (or STEM) problems

• AI-driven tools for enhancing sustainability, resource efficiency, and green engineering

• Natural language user interfaces and collaborative human-AI workflows in engineering design, simulation, optimization and research

• Integration of AI into product lifecycle management and digital twins

• Ethical, legal, and safety considerations in AI-enabled engineering workflows

Preparing Students for the New AI-Driven Workplace

• 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

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

Contributions will be reviewed for quality and relevance to the workshop’s theme.

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IMPORTANT SUBMISSION INSTRUCTION:

• Submissions must be in PDF format.

• When submitting a paper to the TECS-AI Workshop, please click on the Easychair link in the AIxSET Submission webpage and 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) 

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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

Robert Mertens, 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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