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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteEvaluate an AI coding tutor by whether students learn the programming outcomes your course intends to teach—not just by whether they finish assignments faster or submit more working code. Measure learning separately from task performance, inspect how students use the tutor’s feedback, and check that its hinting, access, and permitted-use rules fit the course.
Start with the learning outcomes you need to evaluate
Specify what students should be able to do without relying on the tutor: understand a concept, trace code, diagnose a bug, explain a design choice, or implement a solution. Then choose evidence that directly tests those abilities. The University of Queensland advises that assessment criteria should reflect the learning the task is designed to evidence; Monash likewise frames assessment around obtaining valid evidence of learning when students can access generative AI.
That distinction matters because a correct submission can show that a task was completed without showing that the student can repeat the reasoning independently. Keep evidence of learning separate from evidence of performance on the assisted task.
Sources: University of Queensland guidance, updated 19 March 2026; Monash University’s AI and assessment guidance.
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Measure learning separately from assignment performance
Use a learning measure aligned with the outcome—such as a pre- and post-task knowledge check, code-comprehension exercise, or later independent task—alongside operational measures such as correctness, test coverage, completion, or time. Report the two categories separately. Higher test coverage or faster completion is useful performance evidence, but by itself does not establish durable understanding.
A 2026 randomized Technical University of Munich CS1 study illustrates this separation. It compared a scaffolded tutor, unrestricted ChatGPT, and no-AI support during a 90-minute concurrency exercise, measuring knowledge and code comprehension as well as auto-graded test coverage. Its 275 participants and single exercise describe that study, not a universal estimate for other courses, languages, tutors, or long-term learning.
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Source: Technical University of Munich study (2026).
Inspect what the tutor says and what students do next
Review real or carefully constructed interactions against the course’s teaching goals. Check whether the tutor identifies the relevant misconception, gives a correct and useful next step, and withholds a complete solution when students are meant to practise solving the problem. A tutor that produces plausible explanations can still be a poor fit if it routinely bypasses the intended reasoning.
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Evaluate feedback uptake as well as feedback quality. Observe whether a student tests, adapts, accepts, or rejects a hint—and whether the resulting code or explanation is correct. A 2026 arXiv preprint by Rose Niousha and coauthors analyzes 10,235 code submissions with associated AI-tutor feedback and argues for evaluating students’ actions on feedback, not only judgments of pedagogical quality. Because it is a preprint, treat this as a proposed behavioral evaluation dimension rather than settled consensus.
Source: Niousha et al., arXiv preprint (7 May 2026).
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Compare tutors or configurations on consistent criteria
If you are choosing among products or settings, use the same tasks, student instructions, and outcome measures for each. Where feasible, compare scaffolded AI assistance with unrestricted AI and the course’s usual resources or a no-AI condition. The TUM study is one example of this design, not proof that its setup or results will transfer to a different class.
| Evaluation axis | What to examine |
|---|---|
| Learning evidence | Knowledge, comprehension, transfer, or independent performance aligned to course outcomes. |
| Task performance | Correctness, tests passed, completion, and time; record these separately from learning. |
| Feedback behavior | Whether hints are relevant and correct, and whether students apply them appropriately. |
| Scaffolding | Whether the tutor sustains productive student effort or discloses complete solutions prematurely. |
| Course and access fit | Practical access, transparent rules, an equivalent non-AI route when appropriate, and alignment with assessment expectations. |
These axes synthesize university guidance and study designs; they are not a validated universal scoring instrument. Stanford’s course-planning guidance also prompts instructors to consider alignment with objectives, AI susceptibility, fair grading, multiple forms of assessment, and clear communication.
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Source: Stanford University Teaching Commons.
Set rules that support integrity, fairness, and access
State what AI use is permitted for the course and for each assignment, including any acknowledgement expectation. Align grading criteria with the intended learning rather than trying to infer misconduct from the finished output alone. If responsible AI use is itself a learning outcome, teach and assess it directly. If using AI is optional and not a learning outcome, offer a suitable equivalent non-AI path and consider whether students can practically access any required tools.
These principles guide assessment design; they do not establish that a particular vendor meets an institution’s requirements for privacy, security, accessibility, data retention, or approval. Those questions need to be checked for the specific product and deployment.
Sources: TEQSA’s assessment principles for generative AI use; Stanford course-planning guidance.
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