AI-Enhanced Pedagogy:  A Critical Review of Intelligent Systems for Fostering Multidimensional Student Engagement.

Oluwafemi Samuel Adesanmi; Mohammad Razzaque; Josiah Balota; Olugbenga Akinade

doi:10.14293/FFL26.000020.v1

Abstract

This structured critical review examines how artificial intelligence (AI) systems foster multidimensional
student engagement—behavioral, cognitive, emotional, social, and agentic—in contemporary learning
environments. Using a PRISMA-informed process, we searched Scopus, Web of Science, ERIC, IEEE
Xplore, and ACM Digital Library for diverse English-language studies (2020–2025). Eighty-eight records
were identified; 48 were screened for descriptive trends and 26 were synthesized. We map major AI classes
(intelligent tutoring systems, learning analytics, generative AI, affective computing) to engagement
mechanisms and associated features (adaptivity, feedback, dialogue agents, dashboards, affect sensing).
Evidence shows AI most consistently strengthens cognitive and behavioral engagement through guidance,
personalization, and analytics-driven support, while emotional engagement remains underdeveloped and
inconsistently evaluated. We argue that generative AI can scale agentic engagement via learner-initiated
prompting, co-creation, and self-regulation, but amplifies risks including hallucination, overreliance,
academic integrity, privacy, and bias. We conclude with a human-in-the-loop framework and systemic AI
literacy to align innovation with ethical governance.

Original language	English
Number of pages	1
DOIs	https://doi.org/10.14293/FFL26.000020.v1
Publication status	Published - 30 Jan 2026
Event	Future Facing Learning and AI in Higher Education 2026 - Teesside University, Middlesbrough, United Kingdom Duration: 15 Apr 2026 → 17 Apr 2026 https://www.tees.ac.uk/landing/ffl/index.cfm

Conference

Conference	Future Facing Learning and AI in Higher Education 2026
Abbreviated title	FFL26
Country/Territory	United Kingdom
City	Middlesbrough
Period	15/04/26 → 17/04/26
Internet address	https://www.tees.ac.uk/landing/ffl/index.cfm

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@conference{aae36299e5174e32aa43389e8f429ad5,

title = "AI-Enhanced Pedagogy: A Critical Review of Intelligent Systems for Fostering Multidimensional Student Engagement.",

abstract = "This structured critical review examines how artificial intelligence (AI) systems foster multidimensional student engagement—behavioral, cognitive, emotional, social, and agentic—in contemporary learning environments. Using a PRISMA-informed process, we searched Scopus, Web of Science, ERIC, IEEE Xplore, and ACM Digital Library for diverse English-language studies (2020–2025). Eighty-eight records were identified; 48 were screened for descriptive trends and 26 were synthesized. We map major AI classes (intelligent tutoring systems, learning analytics, generative AI, affective computing) to engagement mechanisms and associated features (adaptivity, feedback, dialogue agents, dashboards, affect sensing). Evidence shows AI most consistently strengthens cognitive and behavioral engagement through guidance, personalization, and analytics-driven support, while emotional engagement remains underdeveloped and inconsistently evaluated. We argue that generative AI can scale agentic engagement via learner-initiated prompting, co-creation, and self-regulation, but amplifies risks including hallucination, overreliance, academic integrity, privacy, and bias. We conclude with a human-in-the-loop framework and systemic AI literacy to align innovation with ethical governance.",

author = "Adesanmi, \{Oluwafemi Samuel\} and Mohammad Razzaque and Josiah Balota and Olugbenga Akinade",

year = "2026",

month = jan,

day = "30",

doi = "10.14293/FFL26.000020.v1",

language = "English",

note = "Future Facing Learning and AI in Higher Education 2026, FFL26 ; Conference date: 15-04-2026 Through 17-04-2026",

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T1 - AI-Enhanced Pedagogy

T2 - Future Facing Learning and AI in Higher Education 2026

AU - Adesanmi, Oluwafemi Samuel

AU - Razzaque, Mohammad

AU - Balota, Josiah

AU - Akinade, Olugbenga

PY - 2026/1/30

Y1 - 2026/1/30

N2 - This structured critical review examines how artificial intelligence (AI) systems foster multidimensional student engagement—behavioral, cognitive, emotional, social, and agentic—in contemporary learning environments. Using a PRISMA-informed process, we searched Scopus, Web of Science, ERIC, IEEE Xplore, and ACM Digital Library for diverse English-language studies (2020–2025). Eighty-eight records were identified; 48 were screened for descriptive trends and 26 were synthesized. We map major AI classes (intelligent tutoring systems, learning analytics, generative AI, affective computing) to engagement mechanisms and associated features (adaptivity, feedback, dialogue agents, dashboards, affect sensing). Evidence shows AI most consistently strengthens cognitive and behavioral engagement through guidance, personalization, and analytics-driven support, while emotional engagement remains underdeveloped and inconsistently evaluated. We argue that generative AI can scale agentic engagement via learner-initiated prompting, co-creation, and self-regulation, but amplifies risks including hallucination, overreliance, academic integrity, privacy, and bias. We conclude with a human-in-the-loop framework and systemic AI literacy to align innovation with ethical governance.

AB - This structured critical review examines how artificial intelligence (AI) systems foster multidimensional student engagement—behavioral, cognitive, emotional, social, and agentic—in contemporary learning environments. Using a PRISMA-informed process, we searched Scopus, Web of Science, ERIC, IEEE Xplore, and ACM Digital Library for diverse English-language studies (2020–2025). Eighty-eight records were identified; 48 were screened for descriptive trends and 26 were synthesized. We map major AI classes (intelligent tutoring systems, learning analytics, generative AI, affective computing) to engagement mechanisms and associated features (adaptivity, feedback, dialogue agents, dashboards, affect sensing). Evidence shows AI most consistently strengthens cognitive and behavioral engagement through guidance, personalization, and analytics-driven support, while emotional engagement remains underdeveloped and inconsistently evaluated. We argue that generative AI can scale agentic engagement via learner-initiated prompting, co-creation, and self-regulation, but amplifies risks including hallucination, overreliance, academic integrity, privacy, and bias. We conclude with a human-in-the-loop framework and systemic AI literacy to align innovation with ethical governance.

U2 - 10.14293/FFL26.000020.v1

DO - 10.14293/FFL26.000020.v1

M3 - Abstract

Y2 - 15 April 2026 through 17 April 2026

ER -

AI-Enhanced Pedagogy: A Critical Review of Intelligent Systems for Fostering Multidimensional Student Engagement.

Abstract

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