Does Chat GPT help coders learn or coast?

Introduction

Programming is one of the clearest tests of whether AI tutors expand human capability or merely create the appearance of it. A learner can ask ChatGPT why a bug occurs, request a step-by-step explanation of a sorting algorithm, or get help understanding an error message. Used this way, AI can function like an endlessly available tutor. But the same system can also generate complete solutions, repair code without explanation, and help students finish assignments they do not really understand.

This creates a problem sometimes described as false mastery: the feeling of competence without the underlying skill. A student may complete more exercises, produce working programs, and receive higher marks, while remaining unable to solve similar problems independently. In the broader AI bloom vision, this distinction matters enormously. If AI makes programming knowledge genuinely more accessible, it could help expand the number of people capable of building software, conducting research, and participating in an increasingly digital civilisation. If it mainly encourages dependency and superficial success, the apparent educational gains may prove fragile. Research increasingly suggests that both outcomes are possible, depending on how AI assistance is designed and used.[pnas.org]pnas.orgGenerative AI without guardrails can harm learningby H Bastani · 2025 · Cited by 191 — Without guardrails, students attempt to use GPT-4…

Tutoring versus answer-copying in programming practice

The central educational question is not whether ChatGPT can write code. It clearly can. The question is whether learners remain mentally engaged while using it.

Programming education has always depended on a certain amount of struggle. Students learn not only by seeing correct solutions but by tracing bugs, forming hypotheses, testing ideas, and discovering why a program fails. These activities build mental models of how software works. When AI shortcuts those steps, students may complete tasks without building those models.

Several studies have found this pattern. Research examining programming students’ use of generative AI observed that many students eventually prompted ChatGPT to generate complete solutions rather than using it as a reasoning partner. The students often prioritised task completion over understanding, especially when deadlines or difficulty increased.[arXiv]arxiv.orgHow Students' Use of ChatGPT Affects their Learning29 Aug 2024 — In this paper, we study how generative AI and specifically large la…

This is not necessarily irrational behaviour. A student under pressure naturally prefers a working answer to a prolonged debugging session. The problem is that programming ability is cumulative. Missing one layer of understanding weakens the next layer. Someone who uses AI to avoid learning loops may later struggle with data structures. Someone who never learns debugging may struggle to maintain larger systems.

Researchers reviewing generative AI in programming education repeatedly identify this tension. Students report benefits such as faster debugging, clearer explanations and reduced frustration. Yet many studies also warn that unrestricted code generation can bypass the very learning processes programming courses are intended to develop.[ScienceDirect]sciencedirect.comAI chatbots in programming education: Students' use in a…by S Groothuijsen · 2024 · Cited by 115 — Results show that stud…[Manuel B]manuelgarcia.infoGarciaTeaching and Learning Computer Programming Using…The emergence of generative AI tools like ChatGPT has sparked investigations in…. Garcia

A useful comparison is the difference between a mathematics tutor who asks guiding questions and a calculator that instantly reveals every answer. Both can be helpful, but they serve different educational purposes. Programming AI can act as either.

Why confidence can outrun actual skill

False mastery becomes particularly dangerous because programming provides strong feedback. Code either runs or it does not. When ChatGPT produces functioning code, learners may reasonably conclude that they understand what happened.

Yet successful output is not the same as transferable knowledge.

A recurring finding across AI-learning research is that performance during assisted practice can diverge from performance after assistance is removed. One of the most discussed studies comes from research on GPT-based tutoring systems in mathematics. Students with unrestricted GPT-4 access solved more practice problems, but later performed worse when they had to work independently. The researchers concluded that many learners had used the model as a crutch rather than a tutor. Guardrails that encouraged reasoning significantly reduced this effect.[pnas.org]pnas.orgGenerative AI without guardrails can harm learningby H Bastani · 2025 · Cited by 191 — Without guardrails, students attempt to use GPT-4…[SSRN]papers.ssrn.comAI Without Guardrails Can Harm Learningby H Bastani · 2024 · Cited by 367 — Without guardrails, students attempt to use GPT-4 as a “crutc…

Although this study focused on mathematics rather than programming, the mechanism is highly relevant to coding education. Programming also requires the gradual acquisition of mental models and problem-solving habits. If learners repeatedly obtain solutions without constructing those models themselves, apparent competence can exceed actual capability.

Researchers studying coding classes have found similar concerns. Across observational and experimental work, AI assistance often improves immediate task completion while producing mixed results for longer-term understanding. In some cases, learners who heavily relied on generated solutions showed weaker knowledge gains than those who used AI more selectively.[arXiv]arxiv.orgHow Students' Use of ChatGPT Affects their Learning29 Aug 2024 — In this paper, we study how generative AI and specifically large la… 2arXiv

This creates a distinctive psychological risk. Learners receive constant signals of success:

Yet many of the underlying skills remain outsourced.

The result is similar to using satellite navigation for every journey. One may reach the destination efficiently while developing little sense of geography. The tool improves immediate performance while reducing the need to build internal maps.

The debugging paradox

One reason programming is such an interesting case is that AI can both strengthen and weaken learning through the same activity.

Debugging illustrates this paradox.

When learners paste an error message into ChatGPT and ask what it means, the model often provides explanations that would otherwise require searching documentation, reading forums or consulting a teacher. This can accelerate understanding and reduce frustration. Students frequently report that AI explanations help them move past obstacles that previously caused them to give up.[ScienceDirect]sciencedirect.comAI chatbots in programming education: Students' use in a…by S Groothuijsen · 2024 · Cited by 115 — Results show that stud…

However, debugging becomes educationally valuable precisely because it forces the learner to reason about cause and effect. If the AI simply identifies the bug and rewrites the code, the learner may never engage with that reasoning process.

The difference is subtle but important:

Learning-oriented debugging

Dependency-oriented debugging

• Fix this.
• Rewrite this function.
• Make this work.

The first pattern builds transferable skill. The second often builds reliance on the tool.

This distinction becomes increasingly important as programming tasks grow more complex. Professional software development involves architecture, judgement, trade-offs, testing and maintenance. Those skills depend heavily on understanding systems rather than merely producing snippets of code.

Why coding creates a special illusion of mastery

Programming has characteristics that make false mastery especially convincing.

First, software is unusually easy to outsource to AI. A complete solution can often be generated from a relatively short prompt. Unlike learning a musical instrument or performing surgery, much of the visible output can be delegated immediately.

Second, beginners often lack the expertise needed to evaluate generated code. They can tell whether a program appears to work, but not necessarily whether it is secure, efficient, maintainable or correct in edge cases.

Third, modern models are highly fluent. They explain code confidently, produce plausible reasoning, and present solutions in a way that resembles expert guidance. This can create an impression of understanding even when important gaps remain.

Some studies of programming learners found that students frequently accepted AI-generated explanations and code despite inaccuracies. The danger is not only factual error. It is misplaced trust. A learner who believes they understand may stop asking questions before genuine comprehension has formed.[ResearchGate]researchgate.netA rapid review of literature amid the rise of generative AI…7 Mar 2025 — This study examines the impact of an AI programmi…

The problem becomes more serious if educational systems reward finished outputs rather than demonstrated reasoning. If assignments can be completed largely through AI assistance, grades may become weaker indicators of actual programming ability. Universities are already confronting this challenge as AI-generated submissions become increasingly difficult to distinguish from human work.[The Guardian]theguardian.comThe Guardian Researchers fool university markers with AI-generated exam papersOut of 33 AI-generated submissions, only one was flagged, and the rest received higher grades compared to real students. This experiment…

Designing coding help that keeps learners active

The encouraging finding from recent research is that false mastery is not an unavoidable outcome.

Design choices matter.

Studies examining AI tutors with educational guardrails suggest that systems can be structured to preserve learning rather than replace it. Instead of immediately providing solutions, tutors can offer hints, ask diagnostic questions, request predictions, or guide students through intermediate steps.[pnas.org]pnas.orgGenerative AI without guardrails can harm learningby H Bastani · 2025 · Cited by 191 — Without guardrails, students attempt to use GPT-4…

Several principles appear especially important.

Make explanation come before answers

A tutor can ask the learner to explain their current understanding before revealing a solution.

For example:

These prompts force retrieval and reflection rather than passive consumption.

Reveal solutions gradually

Educational research has long found value in scaffolding: providing support while keeping the learner engaged.

In coding, this may mean:

The goal is productive struggle rather than total frustration.[pnas.org]pnas.orgGenerative AI without guardrails can harm learningby H Bastani · 2025 · Cited by 191 — Without guardrails, students attempt to use GPT-4…

Require code reading, not just code writing

Many students use AI primarily to generate code. Yet professional developers spend substantial time reading existing codebases.

AI tutors can strengthen understanding by asking learners to trace execution, predict outputs, explain functions, or identify flaws in generated programs.

This shifts attention from production alone to comprehension.

Test without assistance

One of the clearest ways to detect false mastery is to remove the tool periodically.

Can the learner:

If not, apparent progress may be masking dependency.

What this means for AI-enabled human flourishing

Programming education offers a microcosm of a broader question running through the AI bloom debate.

The optimistic vision is not merely that AI performs intellectual work on behalf of humans. It is that AI helps more humans develop intellectual capability themselves. A world where billions of people can learn technical skills faster, receive personalised tutoring, and participate in scientific and technological creation would represent a genuine expansion of human potential.

But abundance of answers is not the same as abundance of understanding.

If AI systems increasingly perform the cognitive work that education is meant to cultivate, societies may produce more outputs without producing correspondingly more expertise. The risk is not that people become incapable overnight. It is that deep competence grows more slowly than surface performance.

Programming is one of the first domains where this tension can be observed directly. Learners can now build projects that would previously have been beyond their reach. Some use that leverage to learn faster and attempt harder challenges. Others use it to bypass learning entirely.

The long-term significance extends beyond coding classes. Future scientists, engineers, doctors, researchers and policymakers may all learn with AI assistance. The lesson emerging from programming education is that the benefits of AI tutoring depend less on whether a system can provide answers and more on whether it keeps people thinking.

In that sense, the strongest educational version of AI bloom is not a future where machines know everything for us. It is a future where machines help far more people acquire knowledge, judgement and creative power for themselves. The difference between those futures may depend on seemingly mundane design choices inside today’s AI tutors.[pnas.org]pnas.orgGenerative AI without guardrails can harm learningby H Bastani · 2025 · Cited by 191 — Without guardrails, students attempt to use GPT-4…[Microsoft]microsoft.comlearning outcomes with genai in the classroom a review of empirical evidenceLearning outcomes with GenAI in the classroom: A review…1 Oct 2025 — This report presents a review of recent empirical evidence of gen…

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Endnotes

1. Source: pnas.org
Link:https://www.pnas.org/doi/10.1073/pnas.2422633122

2. Source: arxiv.org
Link:https://arxiv.org/html/2409.09047v1

3. Source: microsoft.com
Title: learning outcomes with genai in the classroom a review of empirical evidence
Link:https://www.microsoft.com/en-us/research/publication/learning-outcomes-with-genai-in-the-classroom-a-review-of-empirical-evidence/

4. Source: arxiv.org
Link:https://arxiv.org/html/2501.10091v2

5. Source: sciencedirect.com
Link:https://www.sciencedirect.com/science/article/pii/S2666920X24000936

6. Source: manuelgarcia.info
Title: Manuel B
Link:https://manuelgarcia.info/publication/chatgpt-programming-education

7. Source: papers.ssrn.com
Link:https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4895486

8. Source: arxiv.org
Link:https://arxiv.org/abs/2511.13271

9. Source: researchgate.net
Link:https://www.researchgate.net/publication/389490343_Teaching_and_learning_computer_programming_using_ChatGPT_A_rapid_review_of_literature_amid_the_rise_of_generative_AI_technologies

10. Source: arxiv.org
Link:https://arxiv.org/abs/2511.04144

11. Source: researchgate.net
Link:https://www.researchgate.net/publication/374942695_ChatGPT_impacts_in_programming_education_A_recent_literature_overview_that_debates_ChatGPT_responses

12. Source: researchgate.net
Link:https://www.researchgate.net/publication/393011815_Generative_AI_without_guardrails_can_harm_learning_Evidence_from_high_school_mathematics

Published: May 2026

13. Source: arxiv.org
Link:https://arxiv.org/pdf/2501.10091

14. Source: theguardian.com
Title: The Guardian Researchers fool university markers with AI-generated exam papers
Link:https://www.theguardian.com/education/article/2024/jun/26/researchers-fool-university-markers-with-ai-generated-exam-papers

15. Source: theguardian.com
Link:https://www.theguardian.com/education/2025/feb/26/uk-universities-warned-to-stress-test-assessments-as-92-of-students-use-ai

Additional References

16. Source: businessinsider.com
Link:https://www.businessinsider.com/openai-education-brain-rot-productive-struggle-vibe-coding-chatgpt-study

17. Source: chibe.upenn.edu
Link:https://chibe.upenn.edu/publications/generative-ai-without-guardrails-can-harm-learning-evidence-from-high-school-mathematics/

18. Source: linkedin.com
Link:https://www.linkedin.com/posts/hamsa-bastani-4a346955_generative-ai-without-guardrails-can-harm-activity-7343667696540033025-a1Ms

19. Source: reddit.com
Link:https://www.reddit.com/r/learnpython/comments/1kztskw/have_ai_tools_like_chatgpt_made_learning_to_code/

20. Source: psypost.org
Title: unrestricted generative ai harms high school math learning by acting as a crutch
Link:https://www.psypost.org/unrestricted-generative-ai-harms-high-school-math-learning-by-acting-as-a-crutch/

21. Source: youtube.com
Link:https://www.youtube.com/watch?v=JmY8GI5U_pI

22. Source: hamsabastani.github.io
Link:https://hamsabastani.github.io/education_llm.pdf

23. Source: springerprofessional.de
Link:https://www.springerprofessional.de/en/programming-education-with-chatgpt-outcomes-for-beginners-and-in/50859184

24. Source: scribd.com
Title: Bastani Et Al 2025 GAI Without Guardians Can Harm Learning
Link:https://www.scribd.com/document/987492680/Bastani-Et-Al-2025-GAI-Without-Guardians-Can-Harm-Learning

25. Source: ui.adsabs.harvard.edu
Link:https://ui.adsabs.harvard.edu/abs/2025PNAS..12222633B/abstract