An integrated framework for outcome based education and AI supported blended learning in curriculum redesign and intelligent training management

Higher education institutions worldwide face mounting pressure to implement outcome-based education (OBE) at scale while ensuring quality and accountability. Yet OBE often remains resource-intensive, and blended learning implementations frequently lack systematic outcome alignment. Meanwhile, artificial intelligence (AI) applications in education are usually isolated tools (e.g., auto-grading, chatbots) rather than integrated components of curriculum governance. This paper proposes an integrated OBE–AI enhanced blended learning framework, operationalized through the intelligent blended learning system (iBLS). The architecture consists of five layers: (1) OBE curriculum design, (2) granular outcome decomposition, (3) constructive alignment, (4) AI-enhanced blended learning, and (5) continuous quality improvement (CQI). Two AI subsystems are embedded: TutorAI, an assessment engine for question generation, auto-grading, and analytics dashboards; and BotAI, a RAG-based conversational engine providing student support through institutional knowledge retrieval. Both subsystems share a common AI/NLP technological core but are tailored to distinct educational functions. A case study at Thai Nguyen University of Information and Communication Technology, Vietnam (ICTU) implemented the framework across five courses with approximately 2,500 students. Evaluation combined baseline comparison and simulated scalability analysis. Results show that improved outcome transparency (78% of students reported clear weekly targets vs. 42% baseline), stronger alignment ratings (4.3/5 vs. 3.1/5), higher efficiency in assessment (25% reduction in grading workload, 82% item acceptance), enhanced student support (91% query coverage, 88% accuracy), and actionable CQI feedback with 14 course learning outcomes (CLOs) revisions, 12% program learning outcomes (PLOs) updates. Overall pass rates increased from approximately 60% to approximately 80%. The study demonstrates that AI can support the scalability and sustainability of OBE in resource-constrained contexts, bridging theoretical accreditation frameworks and practical implementation. Implications are drawn for institutions, policymakers, and system designers, with future research directions addressing scalability, AI robustness, human adoption, and ethical considerations.