🧠 Cognitive Engagement & Learning Models

Understanding how students shift from passive absorption to active reasoning is critical. Research by Chi and Wylie (2014) provided the ICAP framework that categorizes engagement into levels, offering a roadmap for instructional design. Piaget’s theory (1964) emphasizes that students aged 15–22 undergo a cognitive transition to abstract thinking, which aligns with ICALM’s goal to scaffold learning from surface knowledge to deep connections. Blunt’s work (2014) further shows that techniques like concept mapping improve retention compared to passive reading. These insights directly influenced ICALM’s progression model, which guides students through increasingly sophisticated learning behaviours.

🧩 Concept Mapping & Knowledge Representation

Effective learning often hinges on visualizing complex ideas, and research has consistently shown that concept mapping strengthens comprehension and memory. Hay and Kinchin (2007) demonstrated how maps evolve with student understanding, while Nesbit and Adesope’s meta-analysis (2006) confirmed the cross-disciplinary effectiveness of maps in education. Perry and Winne’s gStudy tool (2006) successfully integrated maps into a web-based learning environment, inspiring ICALM’s automatic flashcard-to-concept map generation. These studies shaped ICALM’s commitment to using concept maps not just as passive visuals, but as active learning tools that organize knowledge meaningfully.

🔗 Knowledge Graphs & AI in Education

Advances in AI have enabled deeper modeling of student knowledge through structured representations like knowledge graphs. Rossiello et al. (2022) introduced KnowGL, a pipeline to extract entity-relation triples, which ICALM uses to map flashcard content into dynamic graphs. RippleNet (Wang et al., 2018) demonstrated how user preferences could be intelligently propagated through graphs to personalize recommendations — the core mechanism behind ICALM’s adaptive learning paths. Finally, Petroni et al. (2019) explored how LLMs can replicate knowledge graph behavior, motivating ICALM’s combination of explicit graphs and chatbot reasoning. This layered knowledge architecture lets ICALM balance structure with flexibility.

🤖 Active Learning, Reflection & Chatbots

To reinforce learning beyond exposure, ICALM leverages active tasks like reflection and dialogue. Barkley’s student engagement techniques (2020) prioritize reflection and peer interaction, core features in ICALM’s session design. Sentence-BERT (Reimers & Gurevych, 2019) allows ICALM to intelligently compare student reflections with AI summaries, giving students immediate, targeted feedback. Finally, Mark et al. (2023) highlighted the trend of shrinking attention spans, prompting ICALM to deliver bite-sized, adaptive content. Together, these research strands ensure that ICALM not only delivers knowledge, but also builds metacognition, self-regulation, and long-term retention.

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📚 Reference