Application of Artificial Intelligence in the BOPPPS Model: A Systematic Literature Review

IF 5.7 2区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Journal of Computer Assisted Learning Pub Date : 2026-06-05 DOI:10.1002/jcal.70272

Yangbao Ma, Changlong Chi, Xiang Yu, Yuzhou Wang, Yali Xu, Changzheng Xin, Lina Wang, Suyu Shi, Liu Yang, Zhengling Wang, Chang Cai

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引用次数: 0

Abstract

Background

The BOPPPS instructional model (Bridge-in, Objective, Pre-assessment, Participatory Learning, Post-assessment, Summary) is widely used in higher education to promote structured and student-centred learning. With the rapid advancement of artificial intelligence (AI), educators are increasingly exploring how AI technologies can support teaching and learning within this framework. However, existing research on AI-integrated BOPPPS instruction remains scattered, and a systematic synthesis of current evidence is still lacking.

Objectives

This study aims to systematically review research on the integration of AI technologies into the BOPPPS instructional model in higher education and examine their effects on teaching and learning outcomes.

Methods

A systematic review was conducted following the PRISMA 2020 guidelines. Multiple academic databases were searched for empirical studies published between 2015 and 2025, and studies were screened using predefined inclusion criteria. Forty-six eligible studies were included and analysed to identify the types of AI technologies used, their application across different BOPPPS stages, and their reported educational impacts.

Results and Conclusions

The review shows a growing body of research, particularly after 2018, integrating technologies such as intelligent tutoring systems, machine learning, virtual and augmented reality, and generative AI into BOPPPS-based teaching. These technologies were reported to support personalised learning, increase student engagement, and facilitate data-informed instruction, leading to improved academic performance, enhanced higher-order thinking skills, and stronger learning motivation. Nevertheless, many studies remain small-scale and quasi-experimental, and challenges related to data privacy, teacher readiness, and equitable access persist. Future research should prioritise large-scale and theory-driven studies to strengthen the evidence base and support effective implementation.

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人工智能在BOPPPS模型中的应用：系统文献综述

BOPPPS教学模式（Bridge-in, Objective, Pre-assessment, Participatory Learning, Post-assessment, Summary）在高等教育中广泛应用，以促进结构化和以学生为中心的学习。随着人工智能（AI）的快速发展，教育工作者越来越多地探索人工智能技术如何在这一框架内支持教学。然而，现有的关于ai集成BOPPPS教学的研究还比较零散，缺乏对现有证据的系统综合。本研究旨在系统回顾人工智能技术融入高等教育BOPPPS教学模式的研究，并考察其对教学成果的影响。方法按照PRISMA 2020指南进行系统评价。在多个学术数据库中检索了2015年至2025年间发表的实证研究，并使用预定义的纳入标准对研究进行筛选。纳入并分析了46项符合条件的研究，以确定所使用的人工智能技术的类型，它们在不同BOPPPS阶段的应用，以及它们报告的教育影响。报告显示，特别是在2018年之后，越来越多的研究将智能辅导系统、机器学习、虚拟和增强现实以及生成式人工智能等技术整合到基于boppps的教学中。据报道，这些技术支持个性化学习，提高学生参与度，促进数据指导，从而提高学习成绩，增强高阶思维技能，增强学习动机。然而，许多研究仍然是小规模和准实验性的，与数据隐私、教师准备和公平获取相关的挑战仍然存在。未来的研究应优先考虑大规模和理论驱动的研究，以加强证据基础，支持有效实施。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computer Assisted Learning EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

9.70

自引率

6.00%

发文量

116

期刊介绍： The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope