Automated Group Discussion Feedback and Scoring with AI

In traditional group discussions, the absence of a human moderator can lead to issues such as dominance by certain individuals, lack of adherence to discussion rules, and insufficient feedback for participants. To address these challenges, an AI-driven group discussion platform is being developed to ensure structured, fair, and productive interactions. The platform will monitor discussions via video, using machine learning to analyze participants' behavior and adherence to ethical guidelines. AI will provide real-time guidance and personalized feedback, assessing individual contributions to highlight strengths and areas for improvement. Comprehensive reports will be generated for each participant, tracking their progress over time. The platform will support two modes: a random call discussion mode for spontaneous interactions and a dedicated college mode for student discussions within an academic context. Timed sessions will ensure focused and efficient discussions. By integrating continuous assessment and rewarding active participation, the platform aims to enhance communication skills, promote ethical behavior, and foster a collaborative learning environment.

Authors

Institutions

Publication Details

Journal
International Research Journal on Advanced Engineering Hub (IRJAEH)
Published
2025-04-02
DOI
https://doi.org/10.47392/irjaeh.2025.0161
Primary Topic
Team Dynamics and Performance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Automated Group Discussion Feedback and Scoring with AI

G. Sivakumar, Sangeetha.V, Khushbu Kumari, C Rashika
International Research Journal on Advanced Engineering Hub (IRJAEH)
Team Dynamics and Performance
article

Automated Group Discussion Feedback and Scoring with AI

G. Sivakumar, Sangeetha.V, Khushbu Kumari, C Rashika
article en

Abstract

In traditional group discussions, the absence of a human moderator can lead to issues such as dominance by certain individuals, lack of adherence to discussion rules, and insufficient feedback for participants. To address these challenges, an AI-driven group discussion platform is being developed to ensure structured, fair, and productive interactions. The platform will monitor discussions via video, using machine learning to analyze participants' behavior and adherence to ethical guidelines. AI will provide real-time guidance and personalized feedback, assessing individual contributions to highlight strengths and areas for improvement. Comprehensive reports will be generated for each participant, tracking their progress over time. The platform will support two modes: a random call discussion mode for spontaneous interactions and a dedicated college mode for student discussions within an academic context. Timed sessions will ensure focused and efficient discussions. By integrating continuous assessment and rewarding active participation, the platform aims to enhance communication skills, promote ethical behavior, and foster a collaborative learning environment.

International Research Journal on Advanced Engineering Hub (IRJAEH)Vol. 3(04)
Rhodes College (US)
Openalex Percentile: Top 93%
Team Dynamics and Performance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Automated Group Discussion Feedback and Scoring with AI — G. Sivakumar, Sangeetha.V, et al. · International Research Journal on Advanced Engineering Hub (IRJAEH) (2025) | TGRS Research Map | TGRS