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arxiv: 2605.09393 · v1 · submitted 2026-05-10 · 💻 cs.SE

Recognition: no theorem link

Prediction Model of Motivators and Demotivators of Integrating Large Language Models in Software Engineering Education: An Empirical Study

Maryam Khan , Muhammad Azeem Akbar , Jussi Kasurinen , Estefan\'ia Mart\'in-Barroso
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Pith reviewed 2026-05-12 04:33 UTC · model grok-4.3

classification 💻 cs.SE
keywords LLM integrationsoftware engineering educationmotivators and demotivatorsprediction modelgenetic algorithm optimizationgovernance mechanismsethical safeguardsstakeholder survey
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The pith

Governance mechanisms like integrity and ethical safeguards should be prioritized for cost-efficient LLM integration in software engineering education.

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

The paper builds a prediction model to find cost-efficient ways to bring large language models into software engineering classes. It draws on survey answers from 126 stakeholders across countries about what encourages or discourages LLM use. These answers train models that guess the chance of high familiarity with LLMs. A genetic algorithm then finds the best balance between these chances and the costs of putting the models in place. Results show that focusing on rules for integrity and ethics first makes the most sense when money is tight.

Core claim

The study develops and validates a prediction model using 19 factors from a survey of 126 stakeholders to estimate the likelihood of high LLM familiarity via Naive Bayes and Logistic Regression. These estimates are optimized with a Genetic Algorithm to identify trade-offs with implementation costs, revealing that governance-related mechanisms, particularly integrity and ethical safeguards, should be prioritized under cost constraints for integrating LLMs into software engineering education.

What carries the argument

A Genetic Algorithm optimization framework that uses probability estimates from probabilistic models to trade off predicted high LLM familiarity against implementation costs at global and category levels.

Load-bearing premise

Likert-scale survey responses from 126 stakeholders can be directly turned into probabilistic predictions of real-world LLM familiarity that can be meaningfully optimized against implementation costs in a genetic algorithm.

What would settle it

A comparison of LLM integration success rates and costs in institutions that prioritize governance safeguards as recommended versus those that focus on other factors first.

Figures

Figures reproduced from arXiv: 2605.09393 by Estefan\'ia Mart\'in-Barroso, Jussi Kasurinen, Maryam Khan, Muhammad Azeem Akbar.

Figure 1
Figure 1. Figure 1: Research Methodology Process and Cognitive Load, and Integration and Practical Implementation. Across these categories, ten sub￾themes were identified: Plagiarism and Intellectual Property Concerns, Over-Reliance on AI in Learning, Reduced Critical Thinking and Problem-Solving, Ethical Concerns in AI-Assisted Learning, Challenges in Evaluating Learning Outcomes, Security, Privacy, and Data Integrity Issues… view at source ↗
Figure 2
Figure 2. Figure 2: Demographic characteristics of the survey respondents ( [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Prediction Model ø Final Insight from Factor-Level Optimization Factor-level optimization indicates that effective LLM integration is not achieved by uniformly addressing all factors in a specific category but by selectively prioritizing those that deliver the highest impact relative to effort. The results show that stronger outcomes are associated with investments in deep learning processes, structured fe… view at source ↗
read the original abstract

Context: Large Language Models (LLMs) are increasingly influencing software engineering practice and education. While prior studies examine their technical performance and classroom use, limited research provides cost-aware and empirically grounded models for systematic institutional integration. Objective: This study develops and validates a prediction model to identify cost-efficient strategies for integrating LLMs into software engineering education using motivating and demotivating factors. Method: Based on our previously developed literature survey taxonomies [1], we operationalized 19 validated factors (9 motivators and 10 demotivators) into a structured survey completed by 126 stakeholders from multiple countries. Likert-scale responses were encoded and used to train probabilistic models (Naive Bayes and Logistic Regression) to estimate the likelihood of high LLM familiarity. The probability estimates were integrated into a Genetic Algorithm (GA)-based optimization framework to model trade-offs between predicted familiarity and implementation cost at global and category levels. Results: Respondents perceived strong benefits in Programming Assistance and Debugging Support and Personalized and Adaptive Learning. Major concerns included Plagiarism and Intellectual Property Concerns, Over-Reliance on AI in Learning, and Reduced Critical Thinking and Problem Solving. Optimization results indicate that governance-related mechanisms, particularly integrity and ethical safeguards, should be prioritized under cost constraints. Conclusions: The study introduces an optimization-informed decision support framework linking stakeholder perceptions with probabilistic modeling and cost-effort analysis. The model supports staged and cost-aware LLM integration grounded in governance stability and pedagogically meaningful development.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper claims to develop and validate a prediction model for motivators and demotivators of integrating LLMs into software engineering education. It operationalizes 19 factors (9 motivators, 10 demotivators) from prior taxonomy into a survey of 126 multi-country stakeholders, encodes Likert responses to train Naive Bayes and Logistic Regression models estimating likelihood of high LLM familiarity, and feeds these probabilities into a Genetic Algorithm to optimize trade-offs between predicted familiarity and implementation costs at global and category levels, concluding that governance mechanisms (especially integrity and ethical safeguards) should be prioritized under cost constraints.

Significance. If the central claims hold after addressing methodological gaps, the work offers a novel optimization-informed decision support framework that empirically links stakeholder perceptions to probabilistic predictions and cost-aware recommendations for LLM integration in SE education. Strengths include the multi-stakeholder survey design and the attempt to combine ML probability estimates with GA-based trade-off modeling, which could provide actionable guidance for institutions balancing benefits like programming assistance against concerns like plagiarism and over-reliance.

major comments (3)
  1. [Method] Method section: The implementation costs used in the Genetic Algorithm objective (at global and category levels) are neither quantified nor sourced; no person-hours, monetary values, effort scales, or derivation method is provided for factors such as 'integrity safeguards' versus 'personalized learning tools'. This is load-bearing for the central optimization claim, as the result that governance mechanisms should be prioritized is not shown to be robust to alternative cost vectors or sensitivity analysis.
  2. [Method] Method section: No performance metrics (e.g., accuracy, AUC, F1), cross-validation procedure, or handling of missing data are reported for the Naive Bayes and Logistic Regression models trained on the 126 Likert-scale responses. Without these, the probability estimates of 'high LLM familiarity' that are directly input to the GA cannot be verified, undermining the prediction model's validity.
  3. [Results] Results section: The optimization conclusion that governance-related mechanisms should be prioritized rests on GA outputs over survey-fitted probabilities; however, the 19 factors originate from the authors' own prior taxonomy [1] and the models are fitted directly to the same responses, so the 'prediction' and optimization largely restate quantities derived from the survey rather than providing independent evidence.
minor comments (2)
  1. [Abstract] The abstract and conclusions refer to 'validated factors' and a 'validated prediction model,' but the manuscript should explicitly state the validation steps (beyond the survey itself) in the Method section for clarity.
  2. Table or figure presenting the GA parameters (population size, generations, cost weights, probability thresholds) is missing or unclear; adding this would improve reproducibility of the optimization results.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thorough and constructive review. We address each major comment point by point below, indicating the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Method] Method section: The implementation costs used in the Genetic Algorithm objective (at global and category levels) are neither quantified nor sourced; no person-hours, monetary values, effort scales, or derivation method is provided for factors such as 'integrity safeguards' versus 'personalized learning tools'. This is load-bearing for the central optimization claim, as the result that governance mechanisms should be prioritized is not shown to be robust to alternative cost vectors or sensitivity analysis.

    Authors: We agree that the costs require explicit quantification and sourcing. In the revised manuscript we will add a dedicated subsection in the Method section that details the cost estimation process, including specific person-hour estimates and relative effort scales for each of the 19 factors. These values will be derived from a combination of published benchmarks on educational technology deployment and consultation with three SE educators. We will also report a sensitivity analysis varying the cost vector by ±20% to confirm that the prioritization of governance mechanisms remains stable. revision: yes

  2. Referee: [Method] Method section: No performance metrics (e.g., accuracy, AUC, F1), cross-validation procedure, or handling of missing data are reported for the Naive Bayes and Logistic Regression models trained on the 126 Likert-scale responses. Without these, the probability estimates of 'high LLM familiarity' that are directly input to the GA cannot be verified, undermining the prediction model's validity.

    Authors: We acknowledge the omission of model evaluation details. The revised paper will include a new subsection reporting accuracy, AUC-ROC, precision, recall, and F1-score for both classifiers, obtained via 5-fold stratified cross-validation. We will also describe the missing-data procedure (listwise deletion after confirming <5% missingness per variable and no systematic patterns) and provide the resulting probability calibration plots to allow verification of the inputs to the genetic algorithm. revision: yes

  3. Referee: [Results] Results section: The optimization conclusion that governance-related mechanisms should be prioritized rests on GA outputs over survey-fitted probabilities; however, the 19 factors originate from the authors' own prior taxonomy [1] and the models are fitted directly to the same responses, so the 'prediction' and optimization largely restate quantities derived from the survey rather than providing independent evidence.

    Authors: We recognize the risk of circularity. While the factors stem from our earlier taxonomy and the models are trained on the collected responses, the framework adds value by converting raw Likert data into calibrated probability estimates of high familiarity and then using those probabilities inside a multi-objective GA to surface cost-aware trade-offs that are not directly observable from descriptive statistics alone. In the revision we will (a) add an explicit comparison in the Results section between GA-derived priorities and a simple baseline ranking by mean Likert scores, and (b) expand the Limitations and Threats to Validity section to discuss the single-dataset nature of the study and the consequent need for future external validation. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper's derivation proceeds from a literature-derived taxonomy (self-cited as prior work) to operationalize 19 factors, followed by collection of new survey data from 126 stakeholders, training of standard Naive Bayes and Logistic Regression models on the Likert responses to produce probability estimates, and application of those estimates within a separate Genetic Algorithm optimization that incorporates implementation costs. The final prioritization result is an output of this empirical modeling and optimization process rather than a quantity equivalent to the inputs by construction. The self-citation supports factor selection but does not justify the central claims, which rest on the independent survey responses and GA outputs. No self-definitional loops, fitted quantities renamed as predictions, or other enumerated circular patterns are present.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on the assumption that the 19 factors identified in the authors' prior work are exhaustive and stable, that Likert responses map linearly to familiarity probabilities, and that implementation costs can be defined and traded off against those probabilities without additional external validation.

free parameters (2)
  • Cost weights in genetic algorithm objective
    Implementation costs are referenced but never quantified or sourced; they function as free parameters that directly shape the optimization output.
  • Probability thresholds for 'high familiarity'
    The logistic regression and Naive Bayes outputs are thresholded to feed the GA; the choice of threshold is not stated and affects which strategies are selected.
axioms (2)
  • domain assumption The 19 motivators and demotivators from the authors' prior taxonomy [1] are valid and sufficient for modeling LLM integration decisions.
    Invoked in the method section when operationalizing the survey.
  • domain assumption Stakeholder Likert responses are unbiased proxies for actual integration outcomes and costs.
    Required to treat survey-derived probabilities as inputs to cost optimization.

pith-pipeline@v0.9.0 · 5588 in / 1656 out tokens · 27564 ms · 2026-05-12T04:33:54.429410+00:00 · methodology

discussion (0)

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Reference graph

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