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Create a professional academic conference-style PowerPoint presentation (12-14 slides) for the following research paper. Use a clean, modern academic template with a consistent …
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Create a professional academic conference-style PowerPoint presentation (12-14 slides) for the following research paper. Use a clean, modern academic template with a consistent color scheme (blues/greens work well for AI/education topics). Include one diagram/chart slide where noted. TITLE: Adaptive Learning Bot: Multi-Modal AI Chatbot with Emotion Awareness AUTHORS: Priyanz Varshney, Pratham Mishra, Meghna Srivastava, Saurabh, Arti Sharma AFFILIATION: Krishna Institute of Engineering & Technology (KIET), Ghaziabad, Delhi-NCR, Uttar Pradesh, India SLIDE STRUCTURE: 1. Title Slide — paper title, authors, affiliation, conference/context 2. Motivation / Problem Statement - Assisting engineering students from varied backgrounds is hard given constantly evolving technical content - Existing Intelligent Tutoring Systems (ITS) are fragmented: affect-aware models read emotion but ignore curriculum; ontology-based systems give correct answers but are emotionally "blind" - No existing single framework unifies affect + curriculum + dialogue + privacy 3. Research Gap (use a simple comparison table) - Compare AutoTutor, SocraticLM, Curriculum-aligned QA, Affective-computing models, Federated personalization vs. ALB across: Affect-Aware, Curriculum-Grounded, Dialogue Style, Privacy-Preserving - Key insight: each existing system is strong in one dimension but weak in others; ALB integrates all 4. Proposed System: AdaptiveLearningBot (ALB) — Overview - Emotion-aware, multi-modal AI framework - Combines affective computing, curriculum ontologies, Socratic dialogue, and privacy-preserving federated learning - Goal: technically correct AND emotionally engaging tutoring 5. System Architecture (5-Layer Design) — include a vertical flow diagram - Layer 1: Presentation (React 18 + TailwindCSS PWA) - Layer 2: Dialogue & Reasoning (Socratic Dialogue Engine + Curriculum Ontology) - Layer 3: Affective & Personalization (Emotion detection, Contextual Bandits, Skill-Gap Analyzer) - Layer 4: Knowledge & Curriculum (RDF triple store — 4,500 courses, 650 ESCO skills) - Layer 5: Privacy & Aggregation (Federated Learning, differential privacy ε=0.5) 6. Affective Computing Engine - 4 privacy-preserving text-based input modalities: Lexical Sentiment (VADER + education dictionary), Timing Features (WPM, pauses), Subject Metrics (topic switches), Historical Baseline - 128-dimensional feature vector fed into a LightGBM ensemble - 6 emotion categories: Joy, Confusion, Frustration, Confidence, Anxiety, Neutral - 84.2% validation accuracy on 10K+ labeled sessions 7. Curriculum-to-Learning Ontology Graph - RDF triple store: ~4,500 courses (CS, Electronics, Mechanical, Civil, Electrical, Chemical) from 50+ AICTE institutions - 650 skills from ESCO taxonomy - Edge weights from faculty surveys, job postings, and student learning data; updated quarterly 8. Socratic Dialogue Engine - Fine-tuned DialoGPT trained on 5,000 Socratic dialogue transcripts - 4 interaction types: Goal Identification, Gap Identification, Misconception Detection, Guided Discovery - Maintains 20-turn dialogue memory with attention-weighted anaphora resolution - 450ms end-to-end latency 9. Contextual Bandit Recommendation Engine - Formulated as a contextual bandit problem: Arms = top-100 candidate courses - LinUCB + Thompson Sampling for exploration-exploitation - Multi-dimensional rewards: usefulness, success rate, skill-gain - 65% faster cold-start convergence (5-10 feedback iterations) vs. traditional RL 10. Experimental Setup - 312 undergraduate engineering students, 6 branches, 8 AICTE-affiliated institutions, 4-week study - Metrics: Hit Rate (Top-1/Top-5), Satisfaction (5-point Likert), Latency, Emotion Detection Accuracy, Learning Gain 11. Results (use a bar chart / comparison table) - Top-1 Hit Rate: 89.4% (ALB) vs 71.2% (Baseline) - Top-5 Hit Rate: 96.1% vs 82.3% - Satisfaction: 4.6/5.0 vs 3.1/5.0 - Emotion Detection Accuracy: 84.2% - Median Latency: 249ms - Learning Gain: +18% vs +6% baseline 12. Discussion: Impact & the "AI Dependency" Risk - Enables institutional policy insight via anonymized "emotional heatmaps" of student stress - Evidence-chip explainability builds student metacognition and agency - Key caution: risk of AI dependency undermining "productive struggle"; ALB counters this by gradually increasing "pedagogical distance" as students improve 13. Limitations & Future Work - Validated only in Indian engineering institutes; needs cross-cultural validation - Text-only emotion detection (no audio/video) - Federated learning has communication overhead on low-bandwidth deployments - Future: cross-continental evaluation, computer-vision-based affect detection, RLHF-tuned Socratic dialogue, emerging-domain ontologies (quantum computing, biomimetic engineering) 14. Conclusion - ALB unifies affective computing, curriculum-grounded ontology, Socratic dialogue, contextual bandits, and federated learning into one holistic ITS - Demonstrated significant gains over baseline across hit rate, satisfaction, and learning outcomes - Positions AI as a scaffold toward student independence, not a replacement for critical thinking DESIGN NOTES: - Use icons for the 5-layer architecture diagram - Use a bar/column chart for slide 11 (Top-1 Hit Rate, Satisfaction, Emotion Accuracy, Learning Gain — ALB vs Baseline) - Keep text minimal per slide (bullet points, not paragraphs); presenter will elaborate verbally - Include slide numbers and a consistent footer with paper title
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This community presentation was shared by Priyanz Varshney as a complete 12-slide example. Create a professional academic conference-style PowerPoint presentation (12-14 slides) for the following research paper. Use a clean, modern academic template with a consistent … The preview lets you review the full sequence in order, rather than judging the design from a single cover image.
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