As part of my ongoing efforts to mentor future innovators in computer science and artificial intelligence, I’m sharing a curated set of project ideas. I invite students, educators, and collaborators to explore these ideas, adapt them, and build upon them.
Each topic includes a project description, minimum expected components, possible advanced features, and recommended skills or technologies. If you develop one of these projects or would like to collaborate, feel free to email folajimiy@wit.edu.
Description:
Design and build an adaptive learning platform for a subject area other than Java, such as thermodynamics, cybersecurity, statistics, discrete mathematics, networking, or another approved domain. The system should support student practice, instructor monitoring, and adaptive sequencing based on learner performance.
Minimum Components:
Student practice interface
Instructor dashboard
Question bank with metadata
Adaptive sequencing engine
Performance logging
Basic analytics
Possible Advanced Features:
Confidence-aware adaptation
Resource recommendation
Mastery or readiness modeling
Topic unlocking
Explanation generation
Recommended Skills / Technologies:
Full-stack web development
Python, JavaScript, React, Flask, Django, or Streamlit
Databases or structured CSV/JSON pipelines
Analytics and visualization
Description:
Build a platform that helps instructors generate, validate, classify, and export educational questions for use in practice or assessment systems. The system should support human review so that AI-generated content can be checked before use.
Minimum Components:
Question generation interface
Validation workflow
Metadata tagging
Duplicate or error detection
Export pipeline
Possible Advanced Features:
Bloom-level classification
Difficulty prediction
Instructor approval interface
Version history and audit trail
Automatic rubric alignment checks
Recommended Skills / Technologies:
NLP or LLM integration
Backend workflow systems
Prompt engineering
Data validation pipelines
Instructor-facing UI
Description:
Build a system that combines student data from multiple platforms and creates a unified learner analytics dashboard. The system should reconcile student identities, normalize data formats, and present meaningful summaries to instructors or researchers.
Minimum Components:
Ingestion from at least two data sources
Identity matching or student reconciliation
Schema normalization
Unified analytics interface
Summary reporting
Possible Advanced Features:
Intervention recommendations
Predictive alerts
Anomaly detection
Exportable research datasets
Recommended Skills / Technologies:
ETL and data engineering
Analytics dashboards
Python, Pandas, and SQL
Visualization libraries
Backend integration
Description:
Create an adaptive assessment engine whose decisions are transparent to students and instructors. The system should sequence questions based on student performance while explaining why each decision was made.
Minimum Components:
Adaptive question sequencing
Student state model
Instructor explanation view
Decision trace logging
Reporting interface
Possible Advanced Features:
Confidence-aware adaptation
Remediation policies
Comparison of multiple decision strategies
Visual explanation of policy decisions
Recommended Skills / Technologies:
Adaptive algorithms
Full-stack development
Analytics and logging
Explainable AI concepts
Visualization
Description:
Build an educational platform that works without continuous internet access and synchronizes data when connectivity is restored. This project is useful for learning environments with unreliable network access.
Minimum Components:
Offline content access
Local storage and logging
Student interaction tracking
Synchronization mechanism
Conflict handling and recovery
Possible Advanced Features:
Offline adaptive practice
Mobile-first interface
Instructor sync dashboard
Recovery monitoring tools
Recommended Skills / Technologies:
Offline-first system design
Local storage and sync architecture
Full-stack development
Mobile or browser-based applications
Description:
Build a platform that allows instructors to define topics, prerequisites, and mastery rules. Students should be able to view or follow a structured skill graph that shows learning pathways and progress.
Minimum Components:
Graph authoring interface
Topic and prerequisite model
Readiness or mastery calculations
Student progress view
Graph visualization
Possible Advanced Features:
Adaptive path recommendations
Intervention triggers
Simulation of student paths
Instructor analytics
Recommended Skills / Technologies:
Graph data structures
Frontend visualization
Backend logic
Educational modeling
Description:
Build a system that presents coding problems, evaluates student solutions, and provides automated feedback or hints. The system should help students practice programming while giving instructors insight into progress and common errors.
Minimum Components:
Coding interface
Problem and test execution
Feedback engine
Progress tracking
Instructor review or reporting
Possible Advanced Features:
Misconception tagging
Adaptive hinting
LLM-generated feedback
Similarity or plagiarism analysis
Recommended Skills / Technologies:
Code execution and sandboxing
Backend systems
Educational analytics
Optional AI-assisted feedback generation
Description:
Build a multi-mode learning platform that supports guided practice, free practice, topic tests, exam simulation, and student review. The goal is to help students prepare more effectively using personalized and trackable study activities.
Minimum Components:
Multiple student modes
Scoring and tracking
Review summaries
Instructor dashboard
Analytics by topic or mode
Possible Advanced Features:
Personalized review plans
Adaptive sequencing
Progress forecasting
Exportable reports
Recommended Skills / Technologies:
Full-stack application development
Adaptive logic
Analytics dashboards
Educational workflow design
Description:
Build a system that recommends learning resources and practice items based on student weaknesses. The platform should help students identify what to study next and help instructors track student needs.
Minimum Components:
Content or resource repository
Topic and subtopic tagging
Student weakness detection
Recommendation engine
Student and instructor views
Possible Advanced Features:
Confidence-aware recommendations
Resource effectiveness tracking
Follow-up assessment generation
Intervention summaries
Recommended Skills / Technologies:
Recommender systems
Information retrieval or search
Analytics
Full-stack development
Description:
Build a platform that allows researchers and instructors to inspect, clean, analyze, and visualize educational interaction data. The system should support reproducible analysis and exportable results.
Minimum Components:
Data ingestion pipeline
Cleaned dataset output
Summary visualizations
Exportable analysis views
Reproducibility support
Possible Advanced Features:
Automatic chart generation
Correlation analysis
Treatment/control comparison
Paper-ready result exports
Recommended Skills / Technologies:
Data engineering
Analytics tooling
Dashboards
Python, R, or visualization libraries
Description:
Build a system where multiple AI agents collaborate to support educational tasks such as question generation, validation, hinting, feedback, and analytics. The system should include a review interface so users can inspect and approve outputs.
Minimum Components:
Orchestrator or controller
At least three specialized agents
Interaction logging
User-facing review interface
Evaluation of agent outputs
Possible Advanced Features:
Fallback logic
Confidence scoring
Consensus or arbitration layer
Instructor approval workflows
Recommended Skills / Technologies:
AI systems architecture
Backend orchestration
APIs or LLM integration
Evaluation pipelines
Description:
Build a dashboard that helps instructors identify students at risk, weak topics, and possible next interventions. The system should turn learning data into actionable teaching support.
Minimum Components:
Student risk indicators
Topic performance summaries
Weak-area detection
Intervention suggestions
History and reporting
Possible Advanced Features:
Predictive alerts
Confidence trend analysis
Email or report generation
Recommendation tracing
Recommended Skills / Technologies:
Dashboards and visualization
Analytics
Rule-based or predictive models
Backend integration
Description:
Create a stealth game where the player must escape a heavily guarded facility while avoiding detection. Guards should patrol, investigate suspicious activity, chase the player when spotted, and return to normal behavior after losing track of the player.
Minimum Components:
Player movement and escape objective
Guard patrol paths
Detection system using vision or sound
Guard behavior states such as patrol, investigate, chase, and return
Win/loss conditions
Possible Advanced Features:
Procedural map generation
Adaptive patrols based on player behavior
Multiple enemy types
Difficulty scaling
Recommended Skills / Technologies:
Unity, Godot, or Unreal
A* pathfinding
Finite state machines or behavior trees
Perception systems
Game design and level design
Description:
Design an arena game where AI-controlled enemies coordinate as a swarm. Enemies should adapt to the player by surrounding, retreating, flanking, or changing tactics based on player movement and combat style.
Minimum Components:
Arena environment
Multiple enemy agents
Multi-agent pathfinding or navigation
Local avoidance
Utility-based or rule-based enemy decisions
Possible Advanced Features:
Procedural enemy wave scaling
Machine learning for group movement
Player behavior modeling
Team-based enemy roles
Recommended Skills / Technologies:
Game AI
Multi-agent navigation
Utility AI
Player modeling
Unity, Godot, or Unreal
Description:
Build a fantasy town game where NPCs respond to the player through dynamic conversations. Dialogue choices should affect trust, story progression, and social puzzles.
Minimum Components:
Branching or rule-based dialogue system
NPC relationship or trust model
Social puzzle mechanics
Story progression system
Player choice tracking
Possible Advanced Features:
Procedural dialogue generation
LLM-based NPC responses
Emotion or mood modeling
Multiple endings
Recommended Skills / Technologies:
Dialogue systems
Behavior trees
Social AI
Narrative design
Optional LLM integration
Description:
Create a forest simulation where AI animals hunt, flee, hide, and respond to terrain, time, and player presence. The project should model survival behavior under partial observability.
Minimum Components:
Animal agents with distinct behaviors
Perception using sight, sound, or smell
Behavior states such as hunt, flee, idle, and hide
Terrain-aware movement
Simulation logging or visualization
Possible Advanced Features:
Adaptive pursuit or evasion
Learning-based detection behavior
Food, energy, or survival systems
Dynamic environment changes
Recommended Skills / Technologies:
Behavior trees or finite state machines
Perception systems
Procedural terrain awareness
Simulation design
Game engine development
Description:
Design a boss enemy that adjusts behavior across multiple fight phases. The boss should observe player tactics and change attacks, defenses, or difficulty in response.
Minimum Components:
Boss fight arena
Multi-phase boss behavior
Player behavior tracking
Attack pattern controller
Difficulty or phase transition logic
Possible Advanced Features:
Procedural attack generation
Reinforcement learning for tuning
Dynamic difficulty adjustment
Explainable boss decision logs
Recommended Skills / Technologies:
FSM or utility-based AI
Player modeling
Adaptive AI
Game balancing
Unity, Godot, or Unreal
Description:
Build a dungeon-crawling experience where layouts, puzzles, traps, and enemies are procedurally generated. AI agents should react intelligently to the generated environment.
Minimum Components:
Procedural dungeon generation
Enemy navigation on dynamic maps
Basic enemy AI
Trap or puzzle logic
Playable dungeon loop
Possible Advanced Features:
Dungeon-solving AI agent
Memory systems for map navigation
Difficulty-controlled generation
Replayable challenge modes
Recommended Skills / Technologies:
Procedural content generation
A* pathfinding
Behavior trees or FSMs
Tilemaps and grid systems
Game development
Description:
Create a simulated village where NPCs follow schedules, form relationships, experience emotions, and respond to player actions. The goal is to simulate believable everyday behavior across multiple autonomous agents.
Minimum Components:
Multiple NPC agents
Daily routine or scheduling system
Social interaction model
Reputation or relationship tracking
Player influence on village dynamics
Possible Advanced Features:
NPC memory of past interactions
Dynamic task assignment
Weather or resource systems
Emergent events
Recommended Skills / Technologies:
FSMs or behavior trees
Social AI
Simulation systems
Data-driven NPC behavior
Game engine development
Description:
Build a puzzle-filled environment where either the player or an AI agent must use search, logic, memory, and perception to escape. The project should demonstrate algorithmic reasoning through gameplay.
Minimum Components:
Puzzle room environment
Search algorithm such as DFS, BFS, or A*
Agent decision-making logic
Environmental interaction system
Win condition and progress tracking
Possible Advanced Features:
Fog-of-war or partial observability
Memory system to avoid failed paths
Procedural puzzle generation
Human-vs-agent comparison mode
Recommended Skills / Technologies:
Search algorithms
FSM or behavior trees
Puzzle design
Procedural generation
Game development
Description:
Create a survival game where the player faces waves of intelligent zombies. Zombie behavior should respond to noise, player movement, visibility, and previous encounters.
Minimum Components:
Player survival mechanics
Zombie wave system
Sound or line-of-sight detection
Dynamic pathfinding
Scoring or survival timer
Possible Advanced Features:
Procedural levels and waves
Learning behavior based on prior encounters
Multiple zombie types
Resource management
Recommended Skills / Technologies:
Pathfinding
Perception systems
Adaptive AI or player modeling
Game balancing
Unity, Godot, or Unreal
Description:
Build a drama simulation where NPCs participate in scripted but reactive scenes. Their choices should change based on relationships, events, mood, alliances, and player involvement.
Minimum Components:
NPC role and scene system
Dialogue or interaction system
Relationship and mood tracking
Event-driven behavior logic
Story state tracking
Possible Advanced Features:
Procedural narrative scenes
Visual social network graph
Dynamic alliances
Player-driven story branching
Recommended Skills / Technologies:
Social AI
Dialogue systems
Behavior trees
Narrative systems
Visualization
Description:
Transform Sokoban-style spatial puzzles into tower defense maps. Walls, paths, and crates should define enemy routes, placement zones, and defensive strategy.
Minimum Components:
Sokoban-style grid movement
Pre-wave crate or turret placement
Enemy path system
Tower or defense mechanics
Scoring or level completion system
Possible Advanced Features:
AI-generated layouts from Sokoban levels
Difficulty curves based on path complexity
Resource management tied to move efficiency
Enemy wave variation
Recommended Skills / Technologies:
Grid-based game design
Tilemaps
Pathfinding
Tower defense mechanics
Unity, Godot, or PuzzleScript
Description:
Repurpose Sokoban puzzles into tactical RPG maps where movement challenges trigger battles, unlock abilities, or power up the player.
Minimum Components:
Sokoban-style puzzle interaction
RPG character progression or ability system
Puzzle-triggered events
Enemies, traps, or environmental changes
Level progression
Possible Advanced Features:
Procedural RPG quest generation
Difficulty scaling based on optimal pushes
Branching campaign structure
Inventory or equipment system
Recommended Skills / Technologies:
Game systems design
Grid-based movement
RPG mechanics
Procedural generation
Unity or Godot
Description:
Use Sokoban levels as the foundation for factory layouts or resource pipeline constraints. Crates can represent resources, goals can represent delivery points, and levels can become logistical challenges.
Minimum Components:
Factory or warehouse grid layout
Resource movement or delivery mechanics
Optimization metrics
Production or delivery goals
User interface for planning and execution
Possible Advanced Features:
Conveyor belts and production chains
Machine-learning agent for layout evaluation
Efficiency scoring
Procedural factory layout generation
Recommended Skills / Technologies:
Simulation systems
Optimization
Grid-based logic
UI design
Optional ML evaluation
Description:
Turn Sokoban-style levels into detective environments where moving crates reveals clues, opens hidden paths, or exposes suspects. Puzzle-solving should drive narrative discovery.
Minimum Components:
Sokoban-style investigation rooms
Clue reveal system
Narrative progression
Character or suspect tracking
Puzzle completion logic
Possible Advanced Features:
Random clue placement using puzzle metadata
Difficulty tied to clue importance
Branching mystery outcomes
Evidence board interface
Recommended Skills / Technologies:
Puzzle design
Narrative systems
Grid-based mechanics
Game state management
UI/UX design
Description:
Apply Sokoban mechanics to a space station or alien world where pushing cargo, modules, or reactors controls environmental states and energy flow.
Minimum Components:
Space-themed Sokoban movement
Modular object or power-unit system
Environmental state changes
Region unlocking or hazard prevention
Level progression
Possible Advanced Features:
Energy-routing puzzles
Oxygen limits or time pressure
Threats or hazards
Dynamic mission objectives
Recommended Skills / Technologies:
Grid-based puzzle mechanics
Environmental systems
Game design
Tilemaps
Unity, Godot, or PuzzleScript
Description:
Develop an intelligent system that allows users to upload a garment image and receive recommended fabric patterns based on color harmony, cultural aesthetics, or fashion trends. The system should preview selected patterns on the garment.
Minimum Components:
Garment image uploader
Fabric pattern recommendation engine
Pattern application or virtual try-on module
Preview and selection interface
Explanation of recommendation logic
Possible Advanced Features:
Cultural or occasion-aware recommendations
User preference learning
Side-by-side pattern comparison
Saved design gallery
Recommended Skills / Technologies:
Computer vision
OpenCV
Recommendation logic
Web interface development
Optional VITON-HD or similar try-on tools
Description:
Create a fashion design generator where users describe garments in natural language. The system should parse the description into structured attributes and generate a visual design.
Minimum Components:
Text input interface
Attribute extractor for garment features
Image generation pipeline
Design preview interface
Final design export or gallery
Possible Advanced Features:
Fabric pattern upload support
Style comparison tools
Multiple design variations
Human feedback loop
Recommended Skills / Technologies:
LLM integration
Prompt engineering
Stable Diffusion, ControlNet, or image generation tools
Streamlit, Gradio, or web development
Description:
Build an AI chatbot that lets users describe garments or styling ideas and then generates or visualizes the requested style using texture blending and image generation tools.
Minimum Components:
Chatbot interface
Fashion term parsing
Texture blending module
Gallery of generated designs
Basic evaluation of realism or usability
Possible Advanced Features:
Multiple style suggestions
User satisfaction ratings
Cultural or occasion-based styling
Editable design attributes
Recommended Skills / Technologies:
Streamlit or Gradio
OpenAI or other LLM APIs
OpenCV
Image generation tools
UI/UX design
Description:
Develop a system where users upload or draw garment sketches and upload a fabric image to apply. The system should detect garment regions and map fabric patterns realistically.
Minimum Components:
Sketch uploader
Fabric pattern upload and preprocessing
Garment region detection or segmentation
Pattern mapping module
Final try-on visualization
Possible Advanced Features:
Edge-aware blending
Fabric warping
Multiple garment templates
Side-by-side before and after views
Recommended Skills / Technologies:
OpenCV
Image segmentation
Texture mapping
ControlNet or sketch-to-image tools
Web prototyping
Description:
Create a stylist assistant app where users upload wardrobe images and receive outfit suggestions powered by fashion rules, image matching, and AI-supported reasoning.
Minimum Components:
Image upload interface
Wardrobe item organizer
Outfit suggestion engine
Fabric or styling options
User feedback collection
Possible Advanced Features:
Mobile-friendly or PWA version
Occasion-aware recommendations
Weather-aware outfit suggestions
Personalized style profiles
Recommended Skills / Technologies:
CLIP or image embeddings
LLM-supported recommendations
Streamlit or web app development
Responsive UI design
Optional mobile/PWA development
Description:
Develop a tool that collects or organizes public fashion images and builds a labeled dataset with metadata such as garment type, color, fabric, and occasion. The project should respect platform policies and ethical data use.
Minimum Components:
Data collection or upload workflow
Auto-tagging module
Dataset format such as CSV/JSON plus images
Dashboard for filtering and review
Export function
Possible Advanced Features:
CLIP-based similarity search
Manual correction workflow
Dataset quality scoring
Bias or diversity analysis
Recommended Skills / Technologies:
Data engineering
Web scraping where permitted
CLIP or image classification
Dataset management
Dashboard development
Description:
Design a tool that promotes cultural heritage by allowing users to apply traditional textile patterns, such as Kente or Ankara, to modern garment templates while considering ethical and respectful design use.
Minimum Components:
Textile pattern dataset
Garment templates
Interactive pattern transfer tool
Cultural context or metadata section
Live demo and documentation
Possible Advanced Features:
3D garment preview
Community-contributed pattern stories
Ethical use guidelines
Cultural impact survey
Recommended Skills / Technologies:
Computer vision
Texture mapping
UI design
Cultural computing
Optional 3D modeling tools
Description:
Develop a tool that evaluates the quality of multiple-choice questions using AI. The system should assess clarity, distractor effectiveness, and cognitive complexity, then provide useful feedback to instructors.
Minimum Components:
MCQ upload or entry interface
Quality rubric
Feature extraction or model pipeline
Quality scoring output
Instructor feedback view
Possible Advanced Features:
BERT-based classification
Distractor effectiveness prediction
Batch evaluation
Actionable revision suggestions
Recommended Skills / Technologies:
NLP
Machine learning
BERT, SVM, or related models
Web interface development
Data annotation
Description:
Create a system that organizes large sets of MCQs into meaningful groups and discovers hidden topics using unsupervised learning. The system should help instructors explore coverage and identify gaps.
Minimum Components:
Question dataset upload
Sentence embedding pipeline
Clustering or topic modeling
Dashboard for exploring clusters
Topic or keyword summaries
Possible Advanced Features:
BERTopic integration
Interactive cluster visualization
Underrepresented topic detection
Manual cluster correction
Recommended Skills / Technologies:
NLP
Sentence transformers
K-means or BERTopic
Data visualization
Dashboard development
Description:
Design an adaptive quiz engine that recommends the next best question or topic based on student performance history. The system should help students practice efficiently and help instructors monitor progress.
Minimum Components:
Student quiz interface
Performance history tracking
Recommendation logic
Question bank with tags
Basic analytics dashboard
Possible Advanced Features:
Collaborative filtering
Reinforcement learning
Mastery modeling
Personalized review plans
Recommended Skills / Technologies:
Recommender systems
Student modeling
Full-stack development
Data handling
Analytics and visualization
Description:
Build a tool that analyzes incorrect student responses to identify common misconceptions in programming or another technical subject. The system should group similar mistakes and help instructors understand patterns.
Minimum Components:
Response data upload
Preprocessing pipeline
Clustering or pattern recognition
Misconception labels or taxonomy
Visual analytics
Possible Advanced Features:
Targeted feedback generation
Instructor validation workflow
Error pattern explanations
Support for multiple programming topics
Recommended Skills / Technologies:
NLP or code analysis
Clustering
Educational data mining
Visualization
Python and data pipelines
Description:
Develop an AI-powered assistant that reviews MCQs and suggests improvements. The system should flag vague wording, weak distractors, formatting issues, or alignment problems and support human approval.
Minimum Components:
MCQ input or upload interface
Rule-based quality checks
AI-enhanced revision suggestions
Side-by-side comparison view
Human approval workflow
Possible Advanced Features:
Rubric alignment checks
Version history
Batch editing
Instructor comment tools
Recommended Skills / Technologies:
LLM integration
Prompt engineering
Rule-based validation
UI design
Backend workflow systems
Description:
Create a predictive model that estimates the difficulty and cognitive level of MCQs. The tool should help instructors balance assessments and filter questions by expected challenge level.
Minimum Components:
Labeled question dataset
Feature extraction pipeline
Difficulty prediction model
Cognitive level estimator
Instructor filtering interface
Possible Advanced Features:
Bloom’s taxonomy classification
Calibration against student performance
Distractor similarity analysis
Model explanation view
Recommended Skills / Technologies:
Machine learning
NLP
Education theory
Web dashboards
Python and scikit-learn
Description:
Build a hybrid system that generates new MCQs from concept prompts and provides explanations or rationales. The system should support instructor editing and review before questions are used.
Minimum Components:
Concept prompt input
Question generation engine
Explanation or rationale generation
Instructor editing interface
Export function
Possible Advanced Features:
Template-based generation
Difficulty or Bloom-level controls
Quality validation checks
Question version history
Recommended Skills / Technologies:
LLM integration
Prompt engineering
Template design
NLP
Full-stack development
Description:
Design a tool that analyzes assessment questions for potential bias, readability concerns, cultural references, gendered language, or exclusionary phrasing. The system should flag issues and suggest inclusive alternatives.
Minimum Components:
Assessment upload interface
Bias and readability checks
Flagged item dashboard
Suggested revisions
Report export
Possible Advanced Features:
Inclusive language rewriting
Cultural context explanations
Human review workflow
Fairness summary metrics
Recommended Skills / Technologies:
NLP
Responsible AI
Text analysis
Dashboards
LLM-supported revision tools
Description:
Build an intelligent tagging system that aligns MCQs to course outcomes, textbook sections, programming concepts, or curriculum standards. The system should support instructor review and correction.
Minimum Components:
Question input or upload
Topic ontology or tag structure
Tag suggestion model
Manual correction interface
Search and filtering tools
Possible Advanced Features:
Course outcome alignment
Confidence scoring
Batch tagging
Integration with quiz-building tools
Recommended Skills / Technologies:
NLP classification
Ontology design
Web development
Data management
Instructor-facing UI
Description:
Develop a search engine that retrieves questions similar to a user’s query, concept, or example question. The system should support instructors in finding duplicates, related items, or reusable questions.
Minimum Components:
Question database
Embedding pipeline
Similarity search engine
Search interface
Filters by topic, difficulty, or format
Possible Advanced Features:
FAISS vector search
Duplicate detection
Similarity explanations
Saved question collections
Recommended Skills / Technologies:
Sentence transformers
Vector databases or FAISS
Search engineering
NLP
Backend development
Description:
Create a system that accepts a code snippet and generates multiple-choice questions about its output, logic, or potential bugs. The system should help instructors create programming assessments efficiently.
Minimum Components:
Code snippet input
Code parsing or analysis
Question generation module
Distractor generation
Instructor review and export
Possible Advanced Features:
Support for multiple programming languages
Static analysis integration
Bug or misconception tagging
Explanation generation
Recommended Skills / Technologies:
Code analysis
LLM integration
Prompt engineering
Programming language tooling
Web development
Description:
Design a tool that enables instructors to generate MCQs during live lectures based on spoken or typed input. The system should support classroom polling, response collection, and visualization.
Minimum Components:
Topic or lecture input
MCQ retrieval or generation module
Real-time polling interface
Student response collection
Results visualization
Possible Advanced Features:
Speech-to-text integration
Live misconception detection
Instructor moderation workflow
Exportable participation reports
Recommended Skills / Technologies:
Live web applications
Speech-to-text or NLP
LLM integration
Visualization
Classroom technology design
Description:
Build an intelligent tutoring platform that supports novice computing students through adaptive practice, confidence-aware feedback, and encouragement strategies designed to improve engagement and persistence, especially for underrepresented learners.
Minimum Components:
Student learning interface
Adaptive quiz or practice engine
Confidence or affect check-ins
Instructor dashboard
Progress and engagement analytics
Possible Advanced Features:
Personalized study plans
Chatbot-based help or hinting
Culturally responsive examples
At-risk alerts and interventions
Recommended Skills / Technologies:
Full-stack web development
Python, JavaScript, React, Flask, or Streamlit
Recommender systems or student modeling
Analytics and visualization
Description:
Design a serious game that helps students understand how AI systems work, including topics such as bias, hallucination, training data, model limitations, and responsible use.
Minimum Components:
Gameplay loop with learning objectives
AI concept modules or levels
Scoring or progression system
Feedback and explanation system
Learning assessment or reflection activity
Possible Advanced Features:
Simulated chatbot or LLM behaviors
Branching ethical decision scenarios
Instructor dashboard
Multiplayer or classroom mode
Recommended Skills / Technologies:
Unity, Godot, or Unreal
Game design and development
Educational assessment design
AI and ethics content modeling
Description:
Build a platform that recommends courses, project topics, internships, or career pathways based on student interests, prior experiences, skill profiles, and academic records.
Minimum Components:
User profile and preference collection
Recommendation engine
Explanation or rationale view
Resource database
Student and advisor dashboard
Possible Advanced Features:
Skill gap analysis
Labor market or job alignment
Internship or mentor matching
Plan comparison tools
Recommended Skills / Technologies:
Recommender systems
Python, Pandas, and SQL
Full-stack application development
Explainable AI and analytics
Description:
Create a dashboard that helps departments track inclusion, retention, mentoring, and student support outcomes in order to strengthen participation in computing programs.
Minimum Components:
Data ingestion from institutional sources
Demographic and participation summaries
Retention or persistence analytics
Mentoring or intervention tracker
Reporting interface
Possible Advanced Features:
Predictive risk indicators
Equity gap analysis
Cohort comparison tools
Automated summary reports
Recommended Skills / Technologies:
Data engineering and ETL
Dashboards and visualization
Python, SQL, Tableau, or Plotly
Privacy-aware analytics design
Description:
Build a learning platform that teaches STEM concepts through culturally relevant examples, local contexts, and optionally multilingual content to increase engagement and relevance.
Minimum Components:
Content delivery interface
Concept and topic tagging
Culturally grounded example library
Student practice module
Instructor content tools
Possible Advanced Features:
Multilingual support
Adaptive content selection
Community-authored content
Learning impact analytics
Recommended Skills / Technologies:
Full-stack development
Instructional design principles
NLP or translation tools
Analytics and visualization
Description:
Create an educational app that adapts content for diverse accessibility needs through captions, audio support, simplified layouts, and alternative interaction modes.
Minimum Components:
Accessible user interface
Multiple content presentation modes
User preference settings
Progress tracking
Instructor or administrator view
Possible Advanced Features:
Voice navigation
Dyslexia-friendly reading mode
Screen-reader optimization
Accessibility audit reports
Recommended Skills / Technologies:
Accessible web or mobile development
HCI and UX design
Frontend engineering
Assistive technology integration
Description:
Build a tool that audits datasets, assessment items, or AI outputs for bias, fairness, transparency, readability, and inclusive language.
Minimum Components:
Data or text upload interface
Rule-based audit checks
Metric summaries
Flagged-item review workflow
Report export
Possible Advanced Features:
LLM-assisted improvement suggestions
Benchmark comparison tools
Dataset documentation templates
Human review and approval workflow
Recommended Skills / Technologies:
NLP and text analysis
Responsible AI methods
Dashboards and reporting
Backend workflow systems
Description:
Build a platform for managing classroom studies with consent workflows, anonymous identifiers, condition assignment, study delivery, and exportable datasets for analysis.
Minimum Components:
Participant registration
Informed consent workflow
Random or rule-based assignment
Study delivery interface
Results export
Possible Advanced Features:
Pre- and post-survey support
Detailed event logging
Dashboard for study monitoring
IRB-ready documentation tools
Recommended Skills / Technologies:
Full-stack development
Databases and data management
Analytics and reporting
Privacy and security-aware design