Ai Collaboration Platform For Performace Tracking

• Author(s):

  Madhumita .V | Ms.A.Sheelavathi | Dharshana C | Harshitha S | Sri Dhanalakshmi .R

• Keywords:

  Artificial Intelligence, Machine Learning, Contribution Tracking, Performance Analysis.

• Abstract:

  Project-based Learning Is An Effective Pedagogical Approach That Fosters Critical Thinking, Problem-solving, And Teamwork Skills. However, Assessing Individual Contributions Within Group Projects Remains A Major Challenge. Traditional Evaluation Methods, Such As Peer Reviews, Instructor Observations, And Self-reports, Are Often Subjective, Inconsistent, And Time-consuming, Leading To Unfair Grading, Disengagement, And Ineffective Teamwork.To Address These Challenges, We Propose An AI-powered Collaboration And Assessment Platform That Objectively Tracks, Analyzes, And Evaluates Student Participation In Real Time. This System Leverages Machine Learning, Natural Language Processing (NLP), And Data Analytics To Monitor Activities Across Various Digital Platforms, Including Shared Documents, Coding Repositories, Task Management Systems, And Communication Tools.The Platform Features Automated Contribution Tracking, Workload Analysis, Engagement Monitoring, And AI-driven Grading Assistance. It Logs Student Activity, Timestamps Document Edits, Tracks Task Completion, And Analyzes Discussion Participation To Generate Transparent And Fair Contribution Scores. NLP-based Sentiment Analysis Assesses Student Involvement In Chats And Discussions, Identifying Passive Engagement Or Teamwork Challenges. Additionally, AI-powered Peer Review Moderation Detects Biases And Inconsistencies In Ratings, Ensuring Fairness.

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Other Details

• Paper id:

  IJSARTV11I4103056

• Published in:

  Volume: 11 Issue: 4 April 2025

• Publication Date:

  2025-04-10

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