About the challenge
Welcome to the IDL Debate Open Hackathon 2025, organized by Agentix, IIT Delhi. This pan-India hackathon aims to bridge the worlds of technology and competitive debating, inviting developers to create innovative solutions that address real challenges in the debating ecosystem.
Competitive debating is a structured form of argumentation where teams argue for and against a given motion. It fosters critical thinking, persuasive communication, and logical reasoningβskills highly valued in academia and professional environments alike. However, the learning curve can be steep, and many aspects of debate organization, training, and adjudication can benefit from technological innovation.
This hackathon offers participants the opportunity to develop solutions that will revolutionize how students learn debating, how debates are judged, how practice sessions are conducted, and how tournaments are managed. Your contributions could potentially impact millions of debaters across India and beyond.
Get started
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Mode: Online
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Team Size: Solo/Up to 3 members
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Duration: 4 weeks
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Special Sessions:
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1-hour sessions on AI Agents and Debating
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Learning Repository resources for participants
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Prize Categories:
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College Students
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School Students
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All further information in the WhatsApp group shown post registration
Prizes
Winner - Track A
Along with additional possibilities of pre-placement offers and internships
Winner - Track B
Along with additional possibilities of pre-placement offers and internships
Winner - Track C
Along with additional possibilities of pre-placement offers and internships
Devpost Achievements
Submitting to this hackathon could earn you:
Judges
Hardik Mahendru
Kamal Kashyap
To be announced
A qualified panel of judges
Judging Criteria
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Track A: Debate Learning & Practice
Educational Value (30%), Gamification Effectiveness (25%), User Experience (20%), Technical Implementation (15%), Scalability and Adaptability (10%) -
Track B: Live Simulated Mock Debates
Transcription Accuracy (20%), Case Preparation Quality (5%), AI Debate Speech Quality (15%), Interactivity (5%), Skill Level Differentiation (15%), Ui (10%), Judging Quality And Feedback Relevance(15%), Multi-format Support (5%), System Performance (10%) -
Track C: Enhanced Tabbying with Gen AI
Tabbycat Integration (25%), Analytics Quality (25%), User Interface (20%), Performance & Scalability (15%), Data Privacy & Security (15%)
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