远程教育杂志2025，Vol.43Issue(1)：46-56,11.DOI:10.15881/j.cnki.cn33-1304/g4.2025.01.005
基于AHP-BPNN方法的高校学生人工智能素养指标体系构建
Development of an Artificial Intelligence Literacy Indicator System for University Students Using a Combined AHP-BPNN Approach
摘要
Abstract
With the rapid growth of artificial intelligence(AI)technology and its increasing use in society,the economy,and daily life,AI literacy has become a critical competency for improving productivity.However,there is still no standardized and well-defined framework for assessing AI literacy among university students.Building on existing AI literacy models and incorporating feed-back from both domestic and international experts,this study develops an AI literacy evaluation framework consisting of four primary dimensions—knowledge and understanding,skills and application,evaluation and creation,and ethics and responsibility—along with 17 secondary indicators.The study used the analytic hierarchy process(AHP)to assign weights to these indicators and validated these weights using a backpropagation neural network(BPNN)model.By combining expert knowledge with data-driven methods,this re-search develops a reliable and practical AI literacy assessment system,providing both theoretical guidance and practical tools for e-valuating university students'AI literacy in China.关键词
人工智能素养/层次分析法/反向传播神经网络/人工智能教育/学习评价Key words
Artificial intelligence literacy/Analytic hierarchy process(AHP)/Backpropagation neural network(BPNN)/Artificial intelligence education(AIED)/Learning assessment分类
社会科学引用本文复制引用
丁继红,郭丽媛,张文轩,刘华中..基于AHP-BPNN方法的高校学生人工智能素养指标体系构建[J].远程教育杂志,2025,43(1):46-56,11.基金项目
本文系国家自然科学基金面上项目"双螺旋协作学习过程多模态分析与全息数字画像及精准干预研究"(项目编号:62277012)和"教育大数据跨模态融合与多场景高效预测及其可解释性研究"(项目编号:62177013)的阶段性研究成果. （项目编号:62277012）
