Teaching as Training: Incremental and Iterative AI Skill Development
We presented our contribution “Teaching as Training: Iterative and Incremental AI Skill Development” () at the EURECA-PRO Education & Research Days in Hasselt, held under the theme Glocalising Universities: A Shifting Horizon. This is joint work with Jolien Notermans (Department of Educational Development, Policy and Quality Assurance) and Sarah Doumen (Faculty of Sciences) at Hasselt University. More details on the publication page. The visual story is generated using StoryBookly.
The Challenge: Teaching AI Skills to Diverse Student Populations
In a master-level Computer Science course on Human-AI Interaction, students arrive with different levels of prior knowledge about AI. Traditional teaching formats with fixed deadlines and one-shot exams often fail to account for this diversity and can limit opportunities for skill development.
Our Approach: Training Rather Than Teaching
Over the past five years, we have developed and refined an iterative and incremental teaching method for skill-oriented courses that treats education more like training than traditional teaching. The core principles:
- No exams – students are assessed entirely through group assignments and one individual AI project
- Iterations with no strict deadlines – students can iterate on their work as many times as needed during the semester
- Pass/fail system – in each iteration, students have the possibility to earn a “pass” for their assignments, with formative feedback guiding improvement if the assignment is not quiet there yet.
- Incremental complexity – assignments build on each other with increasing complexity and creativity
Students receive compact formative feedback after each iteration, serving both as assessment moments and teaching opportunities. The teaching staff focuses on the relative evolution of students’ skills and knowledge rather than on assignment results alone. There are no penalties for making mistakes – only rewards for finding better solutions.
What Students Said
Student survey data shows strong positive responses across the board. The most valued aspects were the possibility to work iteratively and incrementally, the pass/fail principle, and knowing whether they had passed before the exam period. The absence of “one chance” uncertain evaluations had a positive effect on all students and helped them exceed expectations.
Grounded in Learning Theory
The method is grounded in mastery learning (Garner et al., 2019), formative assessment (Evans et al., 2014; Fukuda et al., 2022), the High Impact Learning that Lasts (HILL) model (Dochy & Segers, 2018), and Self-Determination Theory (Deci & Ryan, 1980; 2008). By fostering motivation and self-determination, the approach balances diverse prior knowledge, builds applicable skills, and sustains student motivation throughout the semester.
The Conference
The EURECA-PRO Education & Research Days - Glocalising Universities: A Shifting Horizon brought together educators, researchers, and policymakers from across the EURECA-PRO alliance and beyond for three days of dialogue around the role of universities in education, research, and society – balancing global ambitions with local realities. The event took place February 3-5, 2026 at the Old Prison of Hasselt University.
Citation
@inproceedings{luytennotermans2026,
author = {Luyten, Kris and Notermans, Jolien and Doumen, Sarah},
title = {Teaching as Training: Iterative and Incremental AI Skill Development},
booktitle = {EURECA-PRO Education & Research Days -- Glocalising Universities: A Shifting Horizon},
year = {2026},
note = {February 3-5, 2026},
publisher = {Hasselt University}
}
References
- Deci, E. L., & Ryan, R. M. (1980). Self-determination theory: When mind mediates behavior. The Journal of Mind and Behavior, 33–43.
- Deci, E. L., & Ryan, R. M. (2008). Self-determination theory: A macrotheory of human motivation, development, and health. Canadian Psychology/Psychologie canadienne, 49(3), 182.
- Dochy, F., & Segers, M. (2018). Creating impact through future learning: The high impact learning that lasts (HILL) model. Routledge.
- Evans, D. J. R., Zeun, P., & Stanier, R. A. (2014). Motivating student learning using a formative assessment journey. Journal of Anatomy, 224(3), 296–303.
- Fukuda, S. T., Lander, B. W., & Pope, C. J. (2022). Formative assessment for learning how to learn: Exploring university student learning experiences. RELC Journal, 53(1), 118–133.
- Garner, J., Denny, P., & Luxton-Reilly, A. (2019). Mastery learning in computer science education. In Proceedings of the Twenty-First Australasian Computing Education Conference (pp. 37–46).