The positive relationship between AI use and motivation should be understood as potential support offered by technology, not as the sole factor determining students' academic success, as motivation re
Research gap analysis derived from 5 education papers in our local library.
The gap
The positive relationship between AI use and motivation should be understood as potential support offered by technology, not as the sole factor determining students' academic success, as motivation remains influenced by various other intern
Consensus across the literature
Clustered from 5 gap mentions across 5 papers via embedding cosine ≥ 0.62.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 5 representative gaps
- The Effect of Artificial Intelligence (AI) Use on Increasing Student Motivation in Thesis Writing (2026) · doi
The positive relationship between AI use and motivation should be understood as potential support offered by technology, not as the sole factor determining students' academic success, as motivation remains influenced by various other internal and external factors.
Keywords: motivation positive relationship understood potential support offered technology sole factor determining students academic success remains - How does artificial intelligence improve ophthalmology education outcomes?—The mediating role of learning motivation and self-efficacy (2026) · doi
AI literacy was found to significantly moderate the AI usage-to-learning motivation pathway but not the AI usage-to-self-efficacy pathway; future research should directly investigate why technology-specific competencies like AI literacy differentially impact motivational versus efficacy-belief outcomes in domain-specific learning contexts.
Keywords: AI literacy moderation learning motivation self-efficacy differential effects domain-specific competency - Determinants of Artificial Intelligence (AI) Utilisation for Teaching and Research among Polytechnic Lecturers in Nigeria (2026) · doi
The regression model explaining 39.6% of variance in AI utilization among Nigerian Polytechnic lecturers leaves 60.4% unexplained; future research should investigate specific mediating variables (infrastructural deficits, cost of access, technical skills, perceived ease of use) that bridge the gap between positive AI perception and actual utilization behavior.
Keywords: AI utilization mediating variables instructor perception technology acceptance model Nigerian polytechnics variance explained - DIDÁCTICO INTELIGENTE: USO DE APPS DE INTELIGENCIA ARTIFICIAL COMO APOYO ACADÉMICO PARA ESTUDIANTES DE TURISMO (2026) · doi
While 40% of students were neutral regarding AI tools' contribution to content retention, the paper does not investigate what specific cognitive mechanisms or pedagogical strategies could improve the gap between perceived usefulness (47% agreement) and actual knowledge retention outcomes.
Keywords: AI-assisted learning content retention cognitive engagement technology acceptance model - The current landscape of teachers artificial intelligence acceptance: Relationships with TPACK and technostress (2026) · doi
The structural equation model explained only modest variance in AI acceptance among teachers, indicating that additional factors beyond TPACK and technostress—specifically perceived usefulness of AI, technological self-efficacy, school support, and ethical concerns—need to be incorporated into comprehensive explanatory models of teacher AI adoption.
Keywords: AI acceptance TPACK technostress perceived usefulness technological self-efficacy school support ethical concerns
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