International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 16, No. 5, 8 Oct. 2026
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An ILM-Grounded Adaptive Framework for Gifted Learners: Integrating LLM Orchestration and Multi-Agent Tutoring
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Index Terms
Cognitive Computing, Gemini API, Large Language Models in Education, Intelligent Agents, Self-Directed Learning, Gifted Education, Independent Learner Model, Adaptive Learning Systems
Abstract
Gifted learners require instructional environments that transcend conventional pedagogical boundaries, offering adaptive challenge, metacognitive scaffolding, and high degrees of learner autonomy. This study presents the design, implementation, and empirical evaluation of the AI-Enhanced Self-Directed Learning (AESDL) framework an integrated adaptive system grounded in Treffinger's Independent Learner Model (ILM) and operationalized through an ensemble of five artificial intelligence (AI) technologies: the Google Gemini API (as central orchestration controller), cognitive computing, multi-agent systems (MAS), expert systems, and automatic speech recognition. The model is built upon four core ILM components Guidance, Self-Development, Enrichment, and Seminars/In-Depth Study each computationally instantiated through dedicated AI subsystems. A quasi-experimental pre-test/post-test control group design was employed with 50 gifted secondary school learners (25 experimental, 25 control) drawn from model gifted-education classrooms in Khartoum State, Sudan. The experimental group received eight weeks of instruction via the AESDL system; the control group received equivalent instruction through conventional electronic resources. Outcome measures self-directed learning skills, problem-solving capacity, and academic enrichment achievement (each scored on a 50-point scale) were analyzed using independent-samples t-tests with Cohen's d effect sizes. Results indicated statistically significant and practically large superiority of the AESDL condition: self-directed learning (t(48) = 7.91, p < .001, d = 2.24), problem-solving (t(48) = 5.52, p < .001, d = 1.56), and academic achievement (t(48) = 6.50, p < .001, d = 1.84). These findings advance the empirical evidence base for AI-mediated gifted education and provide actionable design principles for intelligent adaptive learning systems. The control condition comprised conventional electronic learning resources (digital textbooks, instructional videos, and static online exercises). Ninety-five-percent confidence intervals for the between-group mean differences were [9.13, 15.35], [5.62, 12.06], and [7.02, 13.30] for self-directed learning, problem-solving, and academic achievement, respectively. Given the modest sample (n = 50) and the near-ceiling experimental-group scores, these findings should be interpreted with caution regarding potential ceiling effects and limited external validity, and warrant independent replication with larger, more diverse samples.
Cite This Paper
Mohamed Badawi Mustafa Elkhalifa, "An ILM-Grounded Adaptive Framework for Gifted Learners: Integrating LLM Orchestration and Multi-Agent Tutoring", International Journal of Education and Management Engineering (IJEME), Vol.16, No.5, pp. 55-80, 2026. DOI:10.5815/ijeme.2026.05.05
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