Actionable companion · Assessment & reasoning

Learning with agency: A stylometric machine learning framework for authorship attribution of ChatGPT and student generated texts

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): Age / stage not established. Verify the participant population and task before transferring the findings.

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

Abstract Introduction The surge of generative artificial intelligence (AI) has created major challenges in determining authorship, evaluating student learning, and maintaining academic integrity. Distinguishing AI-generated text from human-generated writing has therefore become increasingly important for authorship verification, educational assessment, and academic honesty. Methods This study proposes a machine learning-based stylometric approach for authorship prediction of short-story adaptations written by non-native German language students and generated by ChatGPT. A corpus of 60 texts was created, comprising 30 student-written adaptations and 30 ChatGPT-generated adaptations based on the same source stories. The proposed framework integrates text preprocessing, stylometric feature extraction, feature selection, and machine learning-based classification. Linguistic and stylometric features included lexical diversity, word and character frequencies, sentence and paragraph length distributions, syntactic complexity, function-word usage, punctuation patterns, readability measures, and semantic consistency. Support Vector Machine (SVM), Random Forest, Decision Tree, and Naïve Bayes classifiers were evaluated. Results The linguistic analysis revealed significant differences between the human-generated and AI-generated text categories. Among the evaluated classifiers, the SVM achieved the best classification performance, with an accuracy of 94.8%, precision of 93.7%, recall of 95.2%, and F1-score of 94.4%. Lexical diversity, sentence complexity, function-word frequency, and readability were identified as the most influential features in authorship prediction. Discussion The findings demonstrate the effectiveness of the proposed stylometric framework for automated authorship attribution and AI-generated text detection. The approach has potential applications in educational assessment, plagiarism detection, forensic linguistics, authorship verification, and the development of intelligent tools for supporting academic integrity in multilingual learning environments. Keywords: artificial intelligence, decision tree, feature extraction, feature selection, random forest, support vector machine (SVM)

What kind of evidence is this?

Supporting AI learning science · Design requires full-text appraisal

Stage: Age / stage not established. 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.

Study methods · source passage

To minimize the classification performance degradation caused by the possible redundancy of high-dimensional feature spaces, feature optimization was carried out before the model training.

Limitations and uncertainty · source passage

The performance of the authorship prediction models were evaluated with common classification statistics. Accuracy The accuracy is defined as the percentage of correctly classified texts.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

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.
Days 16–30 · Design. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. Name an accountable owner and set the baseline, success threshold and budget before implementation.
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.
Days 61–90 · Decide. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. 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

Mani P; Adaikalam Arulanandam. A stylometric machine learning framework for authorship attribution of ChatGPT and student generated texts. Frontiers in Artificial Intelligence (2026-08-24). https://doi.org/10.3389/frai.2026.1921847

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.