AI evidence extraction

AI-Supported Learning in Online Discussion Forums: A Scoping Review

Masami Yoshida, Vorasuang Duangchinda, Nammon Ruangrit · The Electronic Journal of e-Learning · 2025

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

5/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.34190/ejel.24.1.4409

Methodology & findings

Study design

Scoping review following the SALSA (search, appraisal, synthesis, and analysis) framework with PRISMA flow diagram for article screening and selection.

Sample

N = 12, 3 groups

Primary method

Qualitative content analysis using community of inquiry theory framework as analytical indicators. Thematic analysis to identify three perspectives on AI roles and three types of man-machine partnerships.

Main result

The review identified twelve articles examining AI in discussion forums. The findings reveal that "AI was predominantly utilized in the form of chatbots and GenAI in discussion forums. Emerging AI roles included virtual teachers, peer/message matching, and automated evaluation." The analysis identified six applications: intelligent tutoring systems, AI-assisted apps, chatbots, automatic formative assessment, AI teaching assistants, and AI-assisted lifelong learning assistants. Additionally, "cognitive presence was observed to be more predominant than social presence, with a particular emphasis on individual learning achievements."

Reports effect sizes.

Research paradigm

Mixed methods (integrating quantitative study characteristics with qualitative synthesis)

Author conclusions

The authors conclude that "AI was predominantly utilized in the form of chatbots and GenAI in discussion forums" and that the domain "is depicted through the complex interplay between technology, pedagogy, and the learning environment. This context is known as entangled pedagogies, which emphasize understanding the interconnections among these elements and their reciprocal influences." They further note that "AI also served as a conduit between students and teachers."

Risk of bias

Publication bias (only published articles in Scopus were searched); Selection bias (predetermined inclusion/exclusion criteria not fully detailed in abstract); Language bias (Scopus search may favor English-language publications); Heterogeneity in reviewed studies (diverse range of fields and contexts); Not explicitly stated; potential risks include publication bias (Scopus-only search), selection bias inherent to inclusion/exclusion criteria application, and potential language bias if non-English articles were excluded.; Publication bias (only peer-reviewed articles from Scopus included); Language bias (no mention of language restrictions or inclusion); Selection bias in inclusion/exclusion criteria application

Open questions raised

The review identifies that the field requires deeper understanding of entangled pedagogies—the interconnections among technology, pedagogy, and learning environment. The authors note substantial diversity across the reviewed articles and suggest need for more integrated understanding of how AI roles (virtual teachers, peer matching, automated evaluation) function within complex learning ecosystems.
The review identifies the need for deeper understanding of entangled pedagogies in AI-supported learning contexts. It suggests future research should examine the complex interplay between technology, pedagogy, and learning environments. The diversity of applications and perspectives indicates a need for more integrated frameworks across different AI implementations in discussion forums.
The review indicates the need for further investigation into how AI supports autonomous student learning in discussion forums, and highlights the complexity of understanding the interconnections between technology, pedagogy, and learning environment.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 54%

Explore related topics

AI-Assisted Discovery Human-AI Collaboration Literature Review