The positive relationship between AI use and motivation should be understood as potential support offered
Research gap analysis derived from 3 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 internal and external factors.
Evidence profile
Sourced from the limitations and inline gaps and future work of the source papers, classified as theory gap, spanning 3 journals.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- The Effect of Artificial Intelligence (AI) Use on Increasing Student Motivation in Thesis Writing (2026) · EDUCASIA Jurnal Pendidikan Pengajaran dan Pembelajaran · 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.
theory gaplimitationsevidence 5/5Keywords: motivation positive relationship understood potential support offered technology sole factor determining students academic success remains - DIDÁCTICO INTELIGENTE: USO DE APPS DE INTELIGENCIA ARTIFICIAL COMO APOYO ACADÉMICO PARA ESTUDIANTES DE TURISMO (2026) · Veredas do Direito · 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.
theory gapinline gapsevidence 5/5Keywords: AI-assisted learning content retention cognitive engagement technology acceptance model - The current landscape of teachers artificial intelligence acceptance: Relationships with TPACK and technostress (2026) · International Journal of Didactical Studies · 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.
theory gapfuture workevidence 5/5Keywords: AI acceptance TPACK technostress perceived usefulness technological self-efficacy school support ethical concerns
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