Project-based learning gives students opportunities to plan, investigate, collaborate, and reconsider their decisions over time. A study by Ayu and colleagues (2026) found that stronger technology-supported project experiences were associated with stronger self-regulated learning among university students. Students’ perceptions of a tool’s usefulness and ease of use helped explain that relationship, although the survey design cannot establish cause and effect.
I am a big fan of project-based learning, especially now that AI can generate so much of what we traditionally ask students to produce. This paper did not investigate generative AI, but I read it with a question in mind: how can we design projects that keep students responsible for managing their learning when tools can do substantial parts of the work?
A finished project leaves questions unanswered
An impressive presentation can tell us something about the quality of a product. Understanding what students learned also requires attention to how they arrived there. Who identified the problem? How did the group choose its sources? What happened when an initial plan failed? Which decisions did students revise, and why?
These questions connect project work with self-regulated learning: setting goals, selecting strategies, managing time, monitoring progress, seeking help, and evaluating one’s work. A project creates occasions for these activities. Whether students actually practise them depends on its design and the support they receive.
The technology supporting a project also deserves scrutiny. A shared workspace might help students coordinate responsibilities, while a confusing interface could absorb time they need for investigation. The paper brings these everyday design choices into a wider discussion of teaching and pedagogy.
Summary of the study: Project-based learning and self-regulation
Ayu and colleagues surveyed 250 undergraduate students enrolled in an educational technology programme in Indonesia. Their questionnaire examined project-based learning experiences, perceived usefulness, perceived ease of use, and self-regulated learning.
The researchers used structural equation modelling, a statistical approach for testing proposed relationships among concepts measured through multiple questionnaire items. The Technology Acceptance Model informed their attention to whether students considered educational technology helpful and manageable.
The analysis found positive relationships between project-based learning and self-regulated learning, both directly and through students’ perceptions of technology. Perceived usefulness and ease of use accounted for part of the relationship; they did not explain all of it.
The authors describe a project environment with schedules, weekly targets, progress indicators, and records of group contributions. They interpret these features as possible supports for planning, monitoring, and reflection. I find the distinction between accepting a tool and using it to manage learning especially useful.
Some of the discussion describes improved learning and changing habits more confidently than the design can establish. All variables were measured through one questionnaire at one point in time.
The study therefore cannot show that project-based learning produced subsequent improvements, or confirm the direction of the proposed relationships. Students’ existing self-regulation could also shape how they experience projects and technology. The authors acknowledge the causal limitations and caution against extending the findings directly to students in other disciplines.
Practical insights: Designing projects around students’ decisions
The authors recommend integrating digital tools across planning, collaboration, monitoring, and reflection, with attention to usability and educational value. My suggested applications below extend that discussion to projects where generative AI may be available. They were not tested in this study.
Give students a reason to use each tool. A shared calendar can support deadline planning; a contribution log can help a group review participation. Explain the learning purpose and provide enough guidance to make the tool usable.
Avoid adding platforms whose role students cannot explain. Perceived ease of use concerns unnecessary interface difficulties; the intellectual work of the project can still be demanding.
Build checkpoints around decisions. Ask students to identify their current goal, describe what they have tried, and explain their next step. Keep these records brief. A checkpoint should help students notice a problem and adjust their approach, without creating an elaborate documentation exercise.
Ask students to evaluate AI suggestions. If AI proposes a research direction or project plan, students could examine its assumptions and compare it with their own priorities. They might explain one suggestion they adopted, one they changed, and one they rejected. This connects with evaluative judgement in the AI age: the learning opportunity lies in deciding what deserves to be used.
Keep evidence of individual learning within group work. Alongside the shared product, invite each student to explain a decision they contributed to, a difficulty they encountered, and what they would do differently. A brief conversation or reflection can provide additional evidence for interpreting their contribution. This also connects with the question of assessment validity when AI is available.
The paper encourages closer attention to what technology enables students to do during a project. In an AI-supported setting, I would look particularly at who sets the goals, checks progress, and decides when the work needs to change. Those responsibilities need a deliberate place in the project design.
Reference
Ayu, S., Ramly, R. A., Mustaji, M., Mariono, A., Muldiyana, M., Nurhidayah, N., & Widarsa, Y. A. (2026). Project-based learning through the technology acceptance model framework: Its impact on self-regulated learning of university students. Frontiers in Education, 11, Article 1879893. https://doi.org/10.3389/feduc.2026.1879893
