A Novel Learning Maturity Model: Using Generative AI Technology
The rapid integration of Generative Artificial Intelligence Technologies (GAIT) across sectors places education at a critical juncture, demanding a paradigm shift in teaching and assessment.This paper introduces the GAIT-based Learning Maturity Model (GAIT-LMM), a conceptual framework designed to strategically leverage GAIT to enhance learning experiences and reimagine pedagogical practices.The GAIT-LMM advances beyond mere technology adoption, offering a structured, multitiered approach to cultivating higher-order thinking, creativity, and ethical AI use.The model comprises three progressive levels of learning maturity.The first, Opportunity to Know (O2K), emphasizes structured inquiry using curated prompts to support foundational understanding and analytical skills.The second, Opportunity to Think (O2T), integrates the Socratic method, encouraging iterative prompt refinement and collaborative dialogue to build critical thinking and questioning skills.The third, Opportunity to Create (O2C), fosters autonomous, project-based learning, enabling learners to construct original knowledge and develop creative problem-solving and strategic thinking abilities.A central innovation of GAIT-LMM is its shift from summative, product-focused assessments to formative, process-oriented evaluations.It promotes assessing Question Intelligence (QI) -the depth and quality of learner-generated questions -over traditional Answer Intelligence (AI).This rubricbased approach, aligned with the CRAFT framework, aims to cultivate metacognitive skills and deeper engagement with learning content.GAIT-LMM represents a transformative pedagogical model that reframes AI as a collaborative partner rather than a threat.By guiding learners from passive knowledge consumption to active knowledge creation, it lays the groundwork for future empirical studies and the continued evolution of AI-enhanced education.
Institutions
- Algoma University (CA)
Publication Details
- Journal
- International Journal of Intelligent Computing Research
- Published
- 2025-06-30
- DOI
- https://doi.org/10.20533/ijicr.2042.4655.2025.0168
- Primary Topic
- Intelligent Tutoring Systems and Adaptive Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Algoma University