The positive relationship between AI use and motivation

The gap

Evidence profile

Sourced from the limitations and open questions and inline gaps and future work of the source papers, classified as theory gap, spanning 5 journals.

Supporting evidence — 5 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 5/5
  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) · Frontiers in Psychology · 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.

  theory gapopen questionsevidence 5/5
  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) · Journal of African Innovation and Advanced Studies · 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.

  theory gapopen questionsevidence 5/5
  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) · 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 5/5
  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) · 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 5/5
  Keywords: AI acceptance TPACK technostress perceived usefulness technological self-efficacy school support ethical concerns

Questions about this gap