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REVIEW 3 major objections 4 minor 51 references

Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education

T0 review · 3 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A survey of 40 STEM undergraduates finds that 54% would use generative AI to solve a physics problem outright, leading the authors to argue that current student use often bypasses the problem-solving process rather than scaffolding it.

desk verdict A transparent small-N survey with a valuable student-faculty comparison, but the 'over half' and 'falls short' claims outrun the data. read the letter →

arxiv 2412.02653 v1 pith:E5VDMWYJ submitted 2024-12-03 physics.ed-ph cs.AI

Karen D. Wang , Zhangyang Wu , L'Nard Tufts II , Carl Wieman , Shima Salehi , Nick Haber This is my paper · ORCID
classification physics.ed-phcs.AI
keywords generativeAISTEMeducationproblem-solvingcompetencycollegestudentspromptingstrategiesChatGPThighersurveyresearch
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether college STEM students use generative AI tools like ChatGPT as a scaffold for learning or as a crutch that replaces their own thinking. Surveying 40 U.S. STEM undergraduates and 28 physics instructors, it finds nearly all students have adopted genAI and use it mainly to save time. When given a physics problem and asked what prompt they would send, 54% of students said they would have the AI solve the problem directly, while 46% asked scaffolded questions. The authors conclude that current student usage often bypasses the problem-solving process and so falls short of building the competency that STEM education aims to develop. This matters because it suggests concrete mismatches between student practice, faculty recommendations, and the design of AI tools for learning.

What carries the argument

The central instrument is a prompt-writing task embedded in the survey: students were given a typical introductory physics problem and asked what prompt they would give to a ChatGPT-like chatbot for help. Open-ended responses were coded into three categories—copy/paste the problem and ask for a solution, copy/paste with added solving instructions, or ask specific scaffolded questions—and this coding produces the study's headline 54% vs 46% split. The second mechanism is a four-part framework of problem-solving aspects (identifying relevant domain knowledge, collecting needed data, executing the plan, verifying correctness), drawn from the authors' prior work, which is used to compare students' perceived helpfulness of genAI with faculty recommendations for each aspect.

What would settle it

A replication with a large, demographically representative sample of U.S. STEM undergraduates using the same prompt-writing task: if fewer than half of respondents used direct-solution prompts, the claim that the slight majority bypasses their own problem-solving would be falsified. A second falsifier would be a controlled experiment in which direct-solution AI users perform as well as scaffolded users on unassisted post-tests.

Watch

Extended reading notes

Core claim

The study's central finding is that a slight majority of college STEM students, 54% of the 40 surveyed, prefer to use generative AI to produce direct solutions to problems rather than to support their own problem-solving. The prompting task—a physics elevator problem—shows 38% of students would copy/paste or paraphrase the problem and ask the AI to solve it, and another 16% would add instructions for solving; only 46% asked questions that could help them figure it out themselves. The paper interprets this as evidence that students' current approaches often bypass the deeper learning processes needed to develop STEM problem-solving competency. It also documents a sharp gap with faculty: less than one-third of the 28 instructors recommended genAI for any of the four problem-solving aspects surveyed (explaining concepts, gathering missing information, calculations, verifying correctness), and only 7% recommended it for calculations. Both groups named misinformation as a top risk, but students were far less worried than faculty about the quality of learning being damaged.

Load-bearing premise

The 40 self-selected online students and 28 volunteer physics instructors are representative enough of U.S. college STEM students and faculty that the reported 54% direct-solution preference and the student–faculty gap generalize beyond this sample.

Editorial extensions

If this is right

  • If direct-solution prompting dominates, students may experience a false sense of fluency, feeling they have learned a problem type when they have only read an AI's solution; the paper likens this to the known gap between perceived and actual learning from passive lectures.
  • Students and faculty are misaligned: students rate genAI helpful for explaining concepts (82.5% helpful) and calculations (65%), while fewer than one-third of faculty recommend either use, so courses need explicit guidance on when genAI use supports versus undermines learning.
  • Because most students rely on free versions of LLMs, colleges that want equitable support should consider providing reliable, education-focused genAI access to all students and teaching critical evaluation of AI output.
  • A promising design direction, flagged by the paper, is genAI-based tutors that scaffold problem-solving without providing direct solutions, following work showing such tutoring can outperform active learning.
  • Students' dominant motive is saving time, so any intervention to change prompting behavior must address the efficiency incentive rather than only warn about risks.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The 54% figure likely understates the prevalence of direct-solution use in real coursework: the prompt task presented a single problem in a low-stakes survey, and the authors themselves note that the self-selected online sample may overrepresent students who are interested in or comfortable with genAI.
  • The faculty-student gap implies that relying on instructor advice alone will not change student behavior; a testable next step is a randomized course-level comparison between a scaffold-only AI tutor and unrestricted ChatGPT access, measuring unassisted problem-solving performance afterward.
  • Students' shared skepticism about using genAI to verify solution correctness suggests a possible natural entry point for training: if students doubt the tool's reliability for checking, instruction could leverage that doubt to teach verification as a human responsibility.
  • The time-saving motive aligns with broader patterns of technology use in higher education, so without structural incentives (assessments that require demonstrated process), students will likely keep defaulting to the fastest route regardless of warnings.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper reports a mixed-methods survey of 40 US STEM undergraduates and 28 physics instructors about how and why students use generative AI (genAI) tools in STEM coursework, with a focus on problem-solving. It finds high adoption rates, common use cases (finding explanations, exploring topics, summarizing readings, helping with problem sets), time-saving as the dominant motivation, and a coded prompt-writing task suggesting that 54% of respondents (20 of 37 who answered) would ask a chatbot to directly solve a physics problem rather than ask scaffolded questions. The paper also compares students' perceived helpfulness of genAI for four problem-solving aspects with faculty recommendations, and reports perceived benefits (personalized support, information retrieval) and risks (misinformation, over-reliance, academic integrity). It concludes that students' current approaches 'often fall short in enhancing their own STEM problem-solving competencies.'

Significance. If treated as an exploratory descriptive study with appropriately hedged conclusions, this paper would provide a timely snapshot of student and instructor perspectives in a rapidly evolving area. Its strengths include the two-group design (students and faculty), the use of a concrete physics problem to elicit prompting behavior, qualitative coding with a reported 83.4% inter-rater agreement, and an explicit acknowledgment of selection bias in the Limitations section. The central weakness is that the headline evaluative claim about problem-solving competency is not supported by the survey data, which contain no learning-outcome measure, no longitudinal data, and no observation of actual interaction with genAI outputs. The paper is therefore more convincing as a description of self-reported use and perceptions than as a basis for the conclusion that current use 'falls short.'

major comments (3)
  1. [Section 4.4, Table 3; Abstract] The claim that 54% of students 'prefer to use genAI to directly solve problems rather than as a tool to support their own problem-solving' rests entirely on a single hypothetical prompt-writing task, and the coding equates the form of the written prompt with students' cognitive engagement. A student who types the problem and asks for a solution may subsequently work through the output, ask follow-up questions, or treat it as a worked example; conversely, a student who asks 'what are the primary factors influencing elevator travel time' may still be offloading central reasoning. No data on follow-up behavior, actual tool use, or learning outcomes are presented. The evaluative conclusion in the Abstract ('often fall short in enhancing their own STEM problem-solving competencies') therefore goes beyond what the prompt categories can support. Please either validate the prompt-to-engagement inference (e.g., with think-aloud protocols, interaction logs, or a follow-up question about what students do with the AI's output) or substantially soften the claims to describe prompting patterns and perceived helpfulness without asserting that competency is or is not enhanced.
  2. [Abstract and Section 4.4] There is a numerical inconsistency in the central result. Table 3 reports 14 students (38%) who would copy/paste and ask AI to solve, and 6 students (16%) who would add instructions for AI to solve, for a total of 20 out of 37 respondents (54%). The Abstract, however, says 'over half of the student participants' reported simply inputting a problem for AI to generate solutions. With N=40, 20 is exactly half, not 'over half,' and the 3 students who did not answer the prompt task are not accounted for in the abstract wording. Please correct the wording, state the denominator explicitly, and clarify why 3 respondents are missing from the analysis.
  3. [Section 6 (Limitations); Abstract and Conclusions] The authors acknowledge in Section 6 that the sample of 40 self-selected Prolific volunteers and 28 APS-listserv faculty may not be representative of the broader US college STEM population, yet the Abstract and Conclusions state population-level claims as though they were established facts (e.g., 'students' current approaches to utilizing genAI tools often fall short'). Given the acknowledged selection bias, the reported percentages (e.g., 54% direct-solve, 85% adoption in STEM courses) are not robust estimates of population prevalence. Please hedge all population-level generalizations throughout, and move the representativeness caveat into the Results framing rather than only the Limitations section.
minor comments (4)
  1. [Section 4.5, Figure 4] The comparison in Figure 4 and the accompanying text contrasts students' 'helpfulness' ratings with instructors' 'recommendation' ratings. These are different constructs, so the 'stark contrast' and 'misalignment' language should be qualified; the gap may reflect differences in the two groups' roles and experiences rather than a direct disagreement about the tools' affordances.
  2. [Section 3.3] Inter-rater reliability is reported only as a mean percent agreement (83.4%). Percent agreement does not account for chance agreement; please report chance-corrected indices (e.g., Cohen's kappa or Krippendorff's alpha) or per-code agreement values.
  3. [Section 4.3] The sentence 'We did not find any significant association between students' year in college and their usage patterns' is based on N=40 and is therefore very low-powered. Please report the test statistic and effect size, or soften the claim to 'no significant association was detected in this small sample.'
  4. [Methods] The full survey instrument is not included in the manuscript or an appendix. For reproducibility and for readers who wish to adapt the prompt-writing task, consider including the complete student and faculty questionnaires as supplementary material.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: this is a descriptive survey study with no derivation chain, fitted parameters, or predictions that reduce to inputs.

full rationale

The paper is an empirical survey study, not a derivation or modeling paper. There are no equations, fitted parameters, or predictions that could be equivalent to the inputs by construction. The central claims—high adoption rates, reported use cases, prompting strategies, perceived helpfulness, and perceived benefits/risks—are direct summaries of self-report data from 40 students and 28 instructors. The 54% 'preferred to use genAI to directly solve problems' figure is a descriptive count from coded open-ended responses in Table 3, not a fitted or predicted quantity. The only notable self-citation is the use of the authors' prior problem-solving framework (Salehi, 2018; Price et al., 2021) to define the four aspects of problem-solving used in the survey items. This is a construct-definition input to the survey design, not a result derived from the current data, and the survey findings do not depend on that prior framework being true: the prompting behaviors, usage frequencies, and ratings stand as descriptive survey results regardless of how one defines problem-solving aspects. The evaluative conclusion that direct-solution prompting 'often falls short' is an interpretive inference, not a derivation, and the paper explicitly acknowledges sample-size and selection-bias limitations in Section 6. No circular step can be quoted because the specific reduction required by the circularity criteria does not appear in the paper.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No free parameters or invented entities are present. The central claims rest on the domain assumptions listed, primarily the representativeness of the sample and the validity of self-report measures, which are explicitly discussed as limitations by the authors.

assumptions (4)
  • domain assumption The four aspects of problem-solving (identifying domain knowledge, collecting data, executing a plan, verifying correctness) constitute a valid operationalization of STEM problem-solving for measuring helpfulness.
    Introduced in Section 3.1 and used in Section 4.5; drawn from the authors' own prior framework (Salehi 2018; Price et al. 2021). A different framework could yield different ratings.
  • domain assumption Self-reported survey behavior accurately reflects actual genAI use and prompting behavior.
    No observational or log data is collected; students describe what they would do with a sample problem rather than performing it with camera or logging. Social desirability or recall errors could bias responses (Section 3.1, 4.4).
  • domain assumption The recruited Prolific and APS listserv samples are representative of U.S. STEM students and faculty.
    Section 3.2; the authors acknowledge in Section 6 that voluntary online participation may introduce selection bias.
  • domain assumption Inter-rater agreement of 83.4% is sufficient to treat the qualitative coding as reliable.
    Section 3.3 reports the mean agreement; no per-question reliability measures or resolution details are provided beyond discussion, so this threshold is an assumption about coding quality.

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Pith. "Pith review of Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education." pith.science (2026). https://pith.science/paper/E5VDMWYJ

@misc{pith2026241202653,
  author       = {Pith},
  title        = {Pith review of: Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/E5VDMWYJ}},
  note         = {Machine review of arXiv:2412.02653}
}
read the original abstract

Developing problem-solving competency is central to Science, Technology, Engineering, and Mathematics (STEM) education, yet translating this priority into effective approaches to problem-solving instruction and assessment remain a significant challenge. The recent proliferation of generative artificial intelligence (genAI) tools like ChatGPT in higher education introduces new considerations about how these tools can help or hinder students' development of STEM problem-solving competency. Our research examines these considerations by studying how and why college students use genAI tools in their STEM coursework, focusing on their problem-solving support. We surveyed 40 STEM college students from diverse U.S. institutions and 28 STEM faculty to understand instructor perspectives on effective genAI tool use and guidance in STEM courses. Our findings reveal high adoption rates and diverse applications of genAI tools among STEM students. The most common use cases include finding explanations, exploring related topics, summarizing readings, and helping with problem-set questions. The primary motivation for using genAI tools was to save time. Moreover, over half of student participants reported simply inputting problems for AI to generate solutions, potentially bypassing their own problem-solving processes. These findings indicate that despite high adoption rates, students' current approaches to utilizing genAI tools often fall short in enhancing their own STEM problem-solving competencies. The study also explored students' and STEM instructors' perceptions of the benefits and risks associated with using genAI tools in STEM education. Our findings provide insights into how to guide students on appropriate genAI use in STEM courses and how to design genAI-based tools to foster students' problem-solving competency.

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