Document Type : Original Research Paper
Authors
1 Department of Educational Administration and Planning, Faculty of Education and Psychology, Shiraz University, Shiraz, Iran
2 Department of Knowledge & Information Sciences, Faculty of Education and Psychology, Shiraz University, Shiraz, Iran
Abstract
Background and Objectives: In recent years, significant advancements in digital technologies and artificial intelligence (AI) have fundamentally revolutionized the structure of higher education. This transformation, coupled with increasing complexities in educational environments, has created new challenges in the professional development of faculty members. Recent empirical studies and theoretical investigations indicate that traditional professional development programs, despite substantial financial investment and institutional commitment, have encountered significant limitations, including a lack of temporal and spatial flexibility, an absence of personalization, limited continuous feedback, and failure to adapt to evolving educational needs. Mobile learning technologies and AI systems, with their advanced capabilities for analyzing complex learning patterns, personalizing educational content based on individual preferences, and providing intelligent real-time feedback, can effectively address these multifaceted challenges. This research aims to design an innovative conceptual framework for AI-based mobile learning tailored to support the systematic self-improvement of faculty teaching competencies in higher education institutions.
Methods: This applied, interdisciplinary study employed a design-based research approach implemented through four iterative cycles: (1) Problem analysis and identification through systematic literature review based on PRISMA guidelines across three domains: mobile learning, artificial intelligence, and professional development with a focus on teaching competencies, resulting in the selection of 37 articles from Scopus, Web of Science, ERIC, and PubMed databases; (2) Design and development of the conceptual framework through synthesis of theoretical and empirical findings using thematic analysis; (3) Validation involving 15 experts in educational technology, artificial intelligence, and higher education fields; and (4) Redesign and finalization of the framework. Research validity and reliability were ensured through a design grounded in robust theoretical foundations, data triangulation, expert validation, and transparency in the research process.
Findings: The proposed framework comprises four key dimensions and 20 sub-components: Input dimension (identifying individual needs and characteristics for developing personalized pathways); Processing dimension (analyzing data using artificial intelligence algorithms); Output dimension (delivering actionable services and content); and Support dimension (providing necessary infrastructure and services). The self-improvement process is designed as a cyclical and continuous framework encompassing five distinct phases: initial assessment, development pathway planning, implementation and learning, continuous evaluation, and reflection and modification. Validation results from 15 experts demonstrated that the framework was significantly supported across all evaluation indicators, including comprehensiveness, applicability, innovation, and logical coherence (p < 0.001).
Conclusion: The presented conceptual framework provides a comprehensive, innovative, and integrated approach for designing intelligent mobile learning systems to support faculty members' self-improvement in teaching competencies. By integrating the four dimensions within a five-stage cyclical process, this framework encompasses all essential components for an intelligent, flexible, and effective learning system. Key strengths include personalized learning pathways, intelligent needs analysis, and flexible access to educational resources. This framework provides a practical foundation for designing and implementing an intelligent mobile learning management system in higher education. Research limitations include validation limited to 15 experts and a lack of experimental implementation to confirm practical effectiveness. For future studies, it is recommended that pilot projects be conducted in higher education institutions and intelligent algorithms be designed for dynamic needs analysis and provision of flexible learning pathways.
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© 2026 The Author(s). This is an open-access article distributed under the terms and conditions of the Creative Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/)
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