Identification of a risk group for student autonomy in the context of artificial intelligence use: a neuro-fuzzy (ANFIS) approach

Authors

Foschan G.I.
Kuban State University, Russian Federation
Litvinsky K.O.
Kuban State University, Russian Federation
Todovyansky A.A.
Kuban State University, Russian Federation

Abstract

This paper addresses the identification of a risk group for reduced student learning autonomy under the use of AI
tools. An interpretable neuro-fuzzy ANFIS (Adaptive Neuro-Fuzzy Inference System) model is applied to estimate risk. Model
quality is evaluated using RMSE/MAE and Spearman’s rank correlation; error diagnostics and permutation-based feature importance are also performed. The strongest contribution to reduced autonomy is associated with submitting assignments
fully completed by AI and the pronounced influence of AI on the learning process

Keywords

learning autonomy, risk group, artificial intelligence, neuro-fuzzy model, ANFIS, interpretable machine learning, permutation importance

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Article Details

Issue

2026 · No. 2 (82)

Section

Digital Economy

Publication History

Submitted 2026-04-16
Published 2026-06-19

How to Cite

[1]
Foschan G.I., Litvinsky K.O., Todovyansky A.A. Identification of a risk group for student autonomy in the context of artificial intelligence use: a neuro-fuzzy (ANFIS) approach // Economics: Theory and Practice. 2026. No 2 (82). С. 56–62. URL: https://etip.kubsu.ru/article/view/871

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Copyright (c) 2026 Galina Ivanovna Foschan, Kirill Olegovich Litvinsky, Andrey Andreevich Todovyansky

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