Y. Mormul, J. Przybyszewski, T. Siriburanon, J. Healy and P. Cuffe, “Gauging the Capability of Artificial Intelligence Chatbot Tools to Answer Textbook Coursework Exercises in Circuit Design Education”, presented at IEEE International Conference on IT in Higher Education and Training, Paris, France, November 2024
Benefit of this resource and how to make the best use of it
Powerful chatbots, based on intensively-trained large language models, have recently become available for consumer use. The ability of such chatbots to provide credible textual responses to sophisticated engineering problems has been demonstrated in various subfields. This paper seeks to gauge the extent to which such a chatbot can be prompted to complete a set of homework and project exercises for university-level courses in analog, digital, mixed-signal, and signal processing classes. The purpose of this paper is to delineate and clearly articulate the present capabilities of artificial intelligence tools to complete coursework tasks across the field of circuit theory. Building on these research findings, this paper suggests practical ways to mitigate artificial intelligence chatbot tools’ description to academic integrity and genuine learning in universities.
This work is licensed under a CC BY-SA license, allowing adaptation and sharing with proper attribution, provided derivative works use the same license.
?
This citation is automatically generated and may require adjustment. Always verify it against your style guide.
Przybyszewski, J., Healy, J., Cuffe, P., Siriburanon, T., & Mormul, Y. (2025). Gauging the capability of artificial intelligence chatbot tools to answer textbook coursework exercises in circuit design education. National Resource Hub (Ireland). Retrieved from: https://hub.teachingandlearning.ie/resource/gauging-the-capability-of-artificial-intelligence-chatbot-tools-to-answer-textbook-coursework-exercises-in-circuit-design-education/ License: Creative Commons Attribution-ShareAlike (CC BY-SA).
Adapting this resource? Share your version!
If you have modified or adopted this resource, share your version here. Tracking adaptations helps us measure impact and connects others with useful updates.
The Technological University of the Shannon (TUS) Compendium of Approaches to Integrating Digital Competencies into the Curriculum exemplifies the University’s commitment to embedding digital capability development across teaching, learning, and assessment.
A 1-page guide, co-created by lecturers, tutors & students on DkIT's MA in Learning and Teaching, showing where GenAI can, and can't, support particular postgraduate research/skill development (*reflecting practice 2025-26). Includes student-informed prompts on responsible, sustainable use. An editable PowerPoint supports adaptation to own context.
MTU researchers, together with national and international researchers, presented local, national, and global insights into the challenges and opportunities of assessment and feedback in work placement settings.
Teaching and Assessment for Learning Checklists aimed at early career & seasoned lecturers. The checklists provide guidance & reliable curated resources for lecturers who want to deliver an engaging and positive learning experience for their students. They act as a guide to check existing approaches, and help analyse how improvements can be made.