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.
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 optimisation 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, optimisation and research
- Integration of AI into product lifecycle management and digital twins
- Ethical, legal, and safety considerations in AI-enabled engineering workflows
- 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 2026 in Laguna Hills, California, USA (hybrid format) on September 28 – 30, 2026. 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.