Evidence map›Paper›PMID 40068170›Full record

ArticleJMIR medical education2025

Leveraging Generative Artificial Intelligence to Improve Motivation and Retrieval in Higher Education Learners.

Noahlana Monzon, Franklin Alan Hays

Abstract read
In one paragraph

Article in JMIR medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Educating minds with generative AI.Communications psychology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Noahlana MonzonDepartment of Nutritional Sciences, University of Oklahoma Health Sciences, 1200 N Stonewall Ave, 3064 Allied Health Building, Oklahoma City, OK, 73117, United States, 1 405 2718001 ext 41182.ORCID 0000-0003-1364-1141
Franklin Alan HaysDepartment of Nutritional Sciences, University of Oklahoma Health Sciences, 1200 N Stonewall Ave, 3064 Allied Health Building, Oklahoma City, OK, 73117, United States, 1 405 2718001 ext 41182.ORCID 0000-0002-2191-558X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: Generative artificial intelligence (GenAI) presents novel approaches to enhance motivation, curriculum structure and development, and learning and retrieval processes for both learners and instructors. Though a focus for this emerging technology is academic misconduct, we sought to leverage GenAI in curriculum structure to facilitate educational outcomes. For instructors, GenAI offers new opportunities in course design and management while reducing time requirements to evaluate outcomes and personalizing learner feedback. These include innovative instructional designs such as flipped classrooms and gamification, enriching teaching methodologies with focused and interactive approaches, and team-based exercise development among others. For learners, GenAI offers unprecedented self-directed learning opportunities, improved cognitive engagement, and effective retrieval practices, leading to enhanced autonomy, motivation, and knowledge retention. Though empowering, this evolving landscape has integration challenges and ethical considerations, including accuracy, technological evolution, loss of learner's voice, and socioeconomic disparities. Our experience demonstrates that the responsible application of GenAI's in educational settings will revolutionize learning practices, making education more accessible and tailored, producing positive motivational outcomes for both learners and instructors. Thus, we argue that leveraging GenAI in educational settings will improve outcomes with implications extending from primary through higher and continuing education paradigms.

Indexed as

Artificial IntelligenceLearningMotivationStudentsCurriculumGenerative Artificial IntelligenceHumansacademic misconductAIchatGPTcognitive engagementcurriculum structureeducational technologyflipped classroomgamificationgenerative artificial intelligencehigher educationinnovationinstructorsinteractive approachlarge language modelslearnerslearningmachine learningmedical educationpersonalized learningretrieval practiceself-directedsocio-economic disparitiestechnologiesuniversity education

Identifiers

PMID40068170
PMCPMC11918979

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.