Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: Automatic generation of learning path for teaching informatics at university

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract The article discusses the problem concept maps are crucial tools for visualizing informatics knowledge in adaptive learning systems. In computer science education, concept maps are essential for visualizing complex knowledge structures and supporting adaptive learning. This study presents a learning path generation (LPG) algorithm designed to improve personalized learning in computer science. By analyzing students’ concept maps, the LPG algorithm effectively distinguishes between groups of students based on their performance and generates customized learning paths. The LPG algorithm uses advanced computational methods to analyze concept maps and learning paths. Then, simplified learning paths are generated using a topological sorting algorithm. By using this method in computer science education, the following objectives can be achieved: Effectively distinguish between groups of students and generate customized learning paths based on their performance. Knowledge acquisition and increased student engagement. Learning path generation can be applied to any subject area. Keywords: Adaptive learning system, Concept map, Informatics, K-means

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Design requires full-text appraisal

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Aynur Aliyeva; Ali Abbasov. Automatic generation of learning path for teaching informatics at university. MethodsX (2025-09-17). https://doi.org/10.1016/j.mex.2025.103634

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.