Article: Design and validation of reinforcement learning - empowered generative AI teaching capability enhancement strategies for diverse majors in higher vocational education Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2026 Vol.18 No.22 pp.32 - 49 Abstract: This study addresses the marked differences in knowledge structures across higher vocational majors. Such disparities prevent generative artificial intelligence (GenAI) teaching support from precise major-specific matching, causing unstable improvements in teaching ability. Multi-professional adaptive generative feedback learning (MPA-GRL) model is proposed. By constructing professional embedded modulation mechanism and hierarchical RL (reinforcement learning) collaborative optimisation strategy, the dynamic adaptation and teaching feedback regulation of GenAI in different professional situations are realised. Results indicated that after a four-month intervention, the experimental group's average teaching ability rose from baseline to 3.90-4.28, an increase of 0.82-0.97 points, compared with only 0.13-0.30 points in the control group. Between-group differences became statistically significant at month four, with p < 0.005 for most majors. The improvement range showed no significant disparity across majors (F = 0.84, p = 0.652), confirming favourable cross-disciplinary stability of the model. Inderscience Publishers - linking academia, business and industry through research

Title: Design and validation of reinforcement learning - empowered generative AI teaching capability enhancement strategies for diverse majors in higher vocational education

Authors: Changzhi Meng; Ting Wang; Ning Zhu; Wenhe Cui

Addresses: Beijing Vocational College of Labor and Social Security, School of People's Livelihood and Well-being, Beijing, 10020, China ' Beijing Vocational College of Labor and Social Security, School of People's Livelihood and Well-being, Beijing, 10020, China ' Beijing SenseTime Technology Development Co., Ltd., Beijing, 100080, China ' Beijing SenseTime Technology Development Co., Ltd., Beijing, 100080, China

Abstract: This study addresses the marked differences in knowledge structures across higher vocational majors. Such disparities prevent generative artificial intelligence (GenAI) teaching support from precise major-specific matching, causing unstable improvements in teaching ability. Multi-professional adaptive generative feedback learning (MPA-GRL) model is proposed. By constructing professional embedded modulation mechanism and hierarchical RL (reinforcement learning) collaborative optimisation strategy, the dynamic adaptation and teaching feedback regulation of GenAI in different professional situations are realised. Results indicated that after a four-month intervention, the experimental group's average teaching ability rose from baseline to 3.90-4.28, an increase of 0.82-0.97 points, compared with only 0.13-0.30 points in the control group. Between-group differences became statistically significant at month four, with p < 0.005 for most majors. The improvement range showed no significant disparity across majors (F = 0.84, p = 0.652), confirming favourable cross-disciplinary stability of the model.

Keywords: multi-specialty teaching strategy in higher vocational education; GenAI; reinforcement learning; embedded modulation; empirical verification.

DOI: 10.1504/IJRIS.2026.155997

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.22, pp.32 - 49

Received: 27 Mar 2026
Accepted: 28 May 2026
Published online: 28 Aug 2026 *

Title: Design and validation of reinforcement learning - empowered generative AI teaching capability enhancement strategies for diverse majors in higher vocational education

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