MARC

LEADER 00000nab a2200000uu 4500
001 3275510552
003 UK-CbPIL
022 |a 2227-7102 
022 |a 2076-3344 
024 7 |a 10.3390/educsci15111515  |2 doi 
035 |a 3275510552 
045 2 |b d20250101  |b d20251231 
084 |a 231457  |2 nlm 
100 1 |a Chick, John C 
245 1 |a AI-Enhanced Computational Thinking: A Comprehensive Review of Ethical Frameworks and Pedagogical Integration for Equitable Higher Education 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a The rapid integration of artificial intelligence technologies into higher education presents unprecedented opportunities for enhancing computational thinking development while simultaneously raising significant concerns about educational equity and algorithmic bias. This comprehensive review examines the intersection of AI integration, computational thinking pedagogy, and diversity, equity, and inclusion imperatives in higher education through a comprehensive narrative review of 167 sources of current literature and theoretical frameworks. From distilling principles from Human–AI Symbiotic Theory (HAIST) and established pedagogical integration models, this review synthesizes evidence-based strategies for ensuring that AI-enhanced computational thinking environments advance rather than undermine educational equity. The analysis reveals that effective AI integration in computational thinking education requires comprehensive frameworks that integrate ethical AI governance with pedagogical design principles, creating practical guidance for institutions seeking to harness AI’s potential while protecting historically marginalized students from algorithmic discrimination. This review contributes to the growing body of knowledge on responsible AI implementation in educational settings and provides actionable recommendations for educators, researchers, and policymakers working to create more effective, engaging, and equitable AI-enhanced learning environments. 
653 |a Problem solving 
653 |a Pedagogy 
653 |a Higher education 
653 |a Collaboration 
653 |a Citation management software 
653 |a Computer science 
653 |a Instructional design 
653 |a Interdisciplinary aspects 
653 |a Educational technology 
653 |a Ethics 
653 |a Generative artificial intelligence 
653 |a Bias 
653 |a Machine learning 
653 |a Tutoring 
653 |a Inclusion 
653 |a Science education 
653 |a Education policy 
653 |a Critical thinking 
653 |a Systematic review 
653 |a Adaptive learning 
653 |a Educational Quality 
653 |a Competence 
653 |a Educational Research 
653 |a Intelligent Tutoring Systems 
653 |a Influence of Technology 
653 |a Cognitive Processes 
653 |a Equal Education 
653 |a Computer Science Education 
653 |a Evidence 
653 |a Access to Education 
653 |a Coding 
653 |a Information Seeking 
653 |a Artificial Intelligence 
653 |a Educational Assessment 
653 |a Elementary Secondary Education 
653 |a Educational Policy 
653 |a Educational Environment 
653 |a Database Management Systems 
653 |a Educational Strategies 
653 |a Educational Equity (Finance) 
653 |a Algorithms 
773 0 |t Education Sciences  |g vol. 15, no. 11 (2025), p. 1515-1560 
786 0 |d ProQuest  |t Education Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3275510552/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3275510552/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3275510552/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch