AI-DRIVEN LEARNING FOR IMC413: ENHANCING MASTERY THROUGH ASKME247
DOI:
https://doi.org/10.35631/JISTM.1144030Keywords:
Adaptive Learning, AI-Driven Learning Educational Technology, Higher Education, IMC413, Information Storage and Retrieval, Self-Directed LearningAbstract
This study assesses AskMe247's instructional viability and pedagogical efficacy, a course-specific AI-driven learning assistant engineered to support higher education students in mastering IMC413 Information Storage and Retrieval. The course presents learning challenges due to its abstract theoretical constructs and dense technical terminology, such as search algorithms, metadata indexing, and retrieval models. Employing a mixed-methods design, empirical data were gathered from 40 respondent of IMC413 students, who had been selected across two distinct academic cohorts using purposive sampling. Quantitative survey metrics evaluating ease of use, conceptual mastery, efficiency, and engagement were combined with qualitative open-ended responses and continuous system usage logs collected over one semester. Results reveal that AskMe247 significantly enhanced student’s self-reported confidence and conceptual comprehension, with over 80% of respondents reporting improved clarity on complex topics previously unaddressed in live lectures due to hesitation. Students described AskMe247 as easing anxiety, acting like a confidence enhancer and serving as a constant personal tutor, while usage logs indicated peak engagement during exam preparation weeks. additionally, the platform showed initial utility in streamlining routine inquiry resolution, thereby lowering repetitive instructional workload. The implication of this research is the research helps educators implement AI tools in classroom settings and outlines simple safety measure for source attribution and fair content use. Instead of relying on generic conversational AI, AskMe247 shows how specialized, adaptive systems can be embedded into technical courses to bridge classroom instruction with self-directed study. The implications of this study show how educators can incorporate domain-tailored AI tools into classroom environments while keeping simple safeguards for source attribution and fair content use. Instead of depending on broad, general-purpose conversational models, AskMe247 demonstrates how adaptive, course-specific AI can connect classroom instruction with self-directed study, encouraging deeper learning and supporting technical curricula.
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