Artificial Intelligence Project Ideas for Computer Science Final Year

Artificial Intelligence project ideas for Computer Science final year students are at the forefront of innovation, combining cutting-edge research with real-world applications.

Whether you’re interested in machine learning, natural language processing, computer vision, or robotics, this list of 97 AI project ideas will spark your creativity and help you select a topic that showcases your skills.

Dive in to explore unique concepts that can become your next A-grade capstone, backed by hands-on guidance if you need extra support.

Machine Learning and Deep Learning Project Ideas

Machine learning and deep learning techniques form the backbone of many AI applications today. These ideas involve supervised or unsupervised learning, neural networks, and predictive modeling, with practical datasets and evaluation metrics. They offer hands-on experience in data-driven problem solving.

• House Price Prediction Model: Build a regression model that predicts house prices based on features like location, size, and age. Use a dataset of real estate listings and explore algorithms such as linear regression, random forests, or neural networks. Evaluate the model by comparing predicted prices to actual market values and analyze which features are most influential.
• Stock Market Trend Forecasting: Create a time-series prediction system to forecast stock prices or trends. Collect historical stock data and use models like LSTM or ARIMA to capture temporal patterns. Experiment with sentiment analysis on financial news for additional signals. The goal is to help investors make data-driven decisions by estimating future market movements.
• Customer Churn Prediction: Develop a classification system to predict whether customers will leave a service or subscription. Use customer usage statistics, demographics, and past interaction data. Train machine learning models (e.g., decision trees or neural networks) to identify churners. This project teaches feature selection and model evaluation (precision/recall) on real-world business data.
• Credit Card Fraud Detection: Implement an anomaly detection model for fraudulent transactions. Work with a dataset of credit card usage and flag outliers. Use classification algorithms or autoencoders to distinguish normal from suspicious patterns. Accuracy and false-positive rates are critical metrics, and the project simulates real challenges in cybersecurity and finance.
• Movie Recommendation Engine: Create a personalized recommendation system for movies or products. Use collaborative filtering or content-based filtering with movie ratings or product descriptions. Build and train a model that suggests items a user might like based on past ratings or user profiles. This project emphasizes model evaluation with metrics like RMSE or precision at K.
• Healthcare Diagnosis Assistant: Build a medical diagnosis model that predicts diseases (e.g., diabetes or heart disease) from patient data. Use publicly available health datasets with patient attributes. Train classifiers to predict a diagnosis and analyze feature importances (like age, blood pressure). Such a project can illustrate machine learning in healthcare and emphasize ethics and accuracy in medical predictions.
• Energy Consumption Forecasting: Design a model to predict electricity usage for homes or buildings. Utilize historical power usage data along with factors like time of day, weather, and occupancy. Try regression models or LSTM networks for time-series forecasting. Predicting energy trends helps develop smarter grids and energy-saving strategies.
• Sentiment Analysis on Social Media: Analyze tweets or product reviews to determine sentiment (positive, negative, neutral). Collect a dataset of social media text and labels. Preprocess the text (tokenization, cleaning), then train a natural language processing model (e.g., logistic regression, LSTM, or transformer) to classify sentiment. This idea teaches text preprocessing and model training on noisy real-world language data.
• Employee Attrition Predictor: Predict employee turnover at a company using HR data. Build a classification model based on features like job satisfaction, salary, and years at company. Training this model highlights important job factors and can help businesses improve retention. Balance metrics such as precision and recall to ensure fair predictions.
• Traffic Flow Optimization: Develop a predictive model for urban traffic congestion. Use traffic sensor data or public datasets to forecast vehicle counts or traffic jams at different times and locations. This project can incorporate regression or classification models, and potentially reinforcement learning for adaptive traffic signal control. Such work offers hands-on experience in smart city applications.
• Crop Yield Prediction: Use machine learning to forecast agricultural crop yields based on environmental data. Gather data such as weather (rainfall, temperature), soil quality, and historical yields. Train regression models to predict future yields. This helps farmers plan resources, making agriculture smarter and more efficient.
• Real-time Spam Filter: Create a real-time spam classification system for emails or messages. Collect a dataset of labeled spam and ham examples. Use feature extraction (e.g., TF-IDF) and train a classifier like Naive Bayes or deep learning to identify spam. Optimize the system to work efficiently on streaming data, teaching students about real-world deployment considerations.
• Loan Approval Predictor: Build a model that predicts if a bank should approve a loan application. Train on past applicant data (income, credit score, debt, etc.). Use classification algorithms and consider fairness by analyzing bias in predictions. Students will practice cleaning financial data and explaining model decisions to non-technical stakeholders.
• Personalized E-Learning Recommendation: Create a recommendation system that suggests personalized learning materials to students. Use data like student profiles, past quiz scores, and content features. Implement collaborative filtering or knowledge tracing models. The system can suggest courses or exercises to optimize learning outcomes, tying machine learning to educational technology.
• Retail Demand Forecasting: Predict future sales or demand for products in a store using historical sales data and promotions. Use time-series forecasting or regression models. Analyze seasonality, trends, and the impact of events or holidays. Accurate demand forecasting helps inventory management and reduces waste.
• Stock Portfolio Optimization: Combine machine learning with finance to optimize an investment portfolio. Predict returns using historical stock data and risk factors. Then use optimization algorithms to allocate assets for maximum return and minimal risk. This project blends predictive analytics with decision-making models.
• Anomaly Detection in Network Traffic: Detect unusual patterns in computer network data. Collect data like packet logs or system metrics. Train unsupervised models (e.g., isolation forest, autoencoders) to flag anomalies that might indicate intrusions. Cybersecurity applications teach data preprocessing and the challenge of imbalanced data.
• Customer Segmentation for Marketing: Use clustering algorithms to segment customers into distinct groups. Take customer purchase histories and demographics, then apply k-means or hierarchical clustering. Visualize clusters and interpret marketing personas. This project highlights unsupervised learning and data visualization.
• Email Organizer Assistant: Build an AI tool to categorize and prioritize incoming emails. Use machine learning to classify emails (work, personal, spam, priority). Train on labeled email datasets or simulate one. The model helps declutter inboxes and showcases NLP classification in a practical setting.
• Music Genre Classification: Classify music tracks by genre using audio features. Extract features (MFCCs, rhythm) from audio files and train a neural network to label genres. This project crosses domains (signal processing + ML) and is engaging for students who enjoy music and programming.
• Network Intrusion Detection System: Develop a system that classifies network packets as malicious or normal. Use publicly available cybersecurity datasets (like KDD Cup). Train classifiers to detect attacks such as DoS or port scans. Emphasize precision and false-negative rates, key in security applications.

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Natural Language Processing (NLP) Project Ideas

Natural language processing (NLP) enables computers to understand and generate human language. These project ideas involve text or speech data, illustrating how to build chatbots, translators, sentiment analyzers, and more.

• Conversational Chatbot Development: Build an intelligent chatbot for customer support or information retrieval. Define intents and entities, then train NLP models (e.g., using RNNs or transformer-based architectures) to understand user queries and provide helpful responses. This project combines NLP understanding with dialogue management in a conversational interface.
• Multilingual Translator Application: Create an app that automatically translates text between multiple languages. Use sequence-to-sequence models or leverage pre-trained translation APIs. Train on parallel corpora for languages of interest. A good final year project could focus on low-resource language pairs or domain-specific translation (e.g., medical).
• Social Media Sentiment Analysis: Analyze the sentiment of tweets or reviews to gauge public opinion. Collect social media data via APIs, preprocess text (remove noise, emojis), and train sentiment classification models. Results can be visualized on dashboards showing positive/negative trends over time, teaching data collection and NLP classification.
• Fake News Detection System: Design an NLP model to flag potentially false news articles or claims. Gather a dataset of verified fake and real news items. Use text classification with features like word embeddings or transformer models (BERT). A robust project also explores the ethics of automated content moderation.
• Automatic Text Summarization Tool: Develop a system that condenses long articles or documents into concise summaries. Implement extractive summarization (selecting key sentences) or abstractive summarization (generating new summary text) using deep learning. Test it on news articles or research papers to demonstrate utility in education or journalism.
• Grammar and Spell Checker: Create an application that suggests corrections for spelling and grammar. Use language models to score candidate corrections. For example, train a sequence model or fine-tune a pre-trained transformer to predict the correct word in context. This project highlights language modeling and sequence prediction.
• Voice Assistant with Speech-to-Text: Build a voice-controlled AI assistant. Combine automatic speech recognition (ASR) to convert voice to text, NLP for intent understanding, and text-to-speech for responses. Use APIs or open-source models for ASR. It simulates products like Siri or Alexa on a smaller scale.
• Question Answering System: Implement a QA system that answers questions from a given text passage. Use models like BiDAF or transformer-based BERT models trained on QA datasets. For example, provide an article and allow users to ask natural language questions about it. Evaluate by the accuracy of the extracted answer spans.
• Resume Parser and Job Matcher: Develop an NLP tool that extracts information from resumes (skills, education) and matches candidates to job descriptions. Use named entity recognition (NER) to parse CVs and similarity measures or classifiers to match jobs. This could be used by HR systems to shortlist applicants.
• Speech Emotion Recognition: Create a system that identifies the speaker’s emotion from audio. Use speech datasets annotated with emotions (happy, sad, angry, etc.). Extract audio features (pitch, tone) or use convolutional networks on spectrograms. This project shows multimodal learning (audio and possibly text) in AI.
• Topic Modeling for Document Clustering: Analyze a collection of articles or papers to find underlying topics. Use algorithms like LDA (Latent Dirichlet Allocation) or embedding-based clustering. Visualize topic distributions and keyword associations. A project could target news archives or academic publications.
• Poetry and Story Generator: Implement a creative AI that writes poetry or short stories. Train recurrent or transformer-based neural networks on text corpora of poems or stories. The system generates new text mimicking the style of the training set. This encourages experimentation with generative language models.
• Language Detection and Translation Tool: Build a utility that detects the language of a given text and translates it to a target language. Combine language classification and translation models. This tool can be useful for travelers or global social media analysis.
• Chatbot for Mental Health Support: Design a chatbot that uses NLP to provide mental health support or resources. Ensure the bot can recognize distress phrases and respond with empathetic language. While not a replacement for professionals, it can refer users to resources. Important aspects include sensitivity and privacy.

Computer Vision Project Ideas

Computer vision projects teach machines to interpret and act on visual data. These ideas involve image or video analysis using techniques like object detection, image segmentation, and pattern recognition.

• Face Recognition Attendance System: Create a secure attendance system that uses facial recognition. Use a webcam to capture faces and a pre-trained face recognition model (like a CNN) to identify registered students. Mark attendance automatically when a known face appears. This project combines computer vision with database integration.
• Facial Emotion Recognition: Build a system that detects a person’s emotion from their facial expression. Use datasets labeled with emotions (smile, frown, etc.). Train a CNN to classify emotions in real time from webcam input. This project can be applied to user experience research or affective computing studies.
• Object Detection and Tracking: Develop a model that detects objects (like cars, pedestrians) in video frames and tracks them. Use algorithms like YOLO or SSD for detection and tracking methods (Kalman filters) for motion. Test on traffic videos or sports footage. It demonstrates object recognition and motion analysis.
• Medical Image Analysis (Disease Detection): Use computer vision to detect anomalies in medical scans, such as tumors in MRI or signs of pneumonia in chest X-rays. Apply deep learning (CNN) techniques and train on medical imaging datasets. This project highlights the impact of AI in healthcare diagnostics.
• Handwritten Text Recognition: Train a model to recognize handwritten characters or digits from images (e.g., the MNIST dataset). Extend to recognizing cursive handwriting or forms. Use convolutional neural networks and explore data augmentation for handwriting variations.
• Autonomous Drone Navigation: Build a vision system to help a drone navigate autonomously. Use onboard cameras to detect obstacles or identify paths. Implement algorithms for real-time obstacle avoidance (e.g., obstacle detection using stereo vision or optical flow). This cross-disciplinary project covers robotics and vision.
• Self-Driving Car Lane Detection: Implement a vision module that identifies lane boundaries in images from a car’s camera. Apply edge detection and Hough transforms or train a segmentation network. The system helps simulate self-driving features, allowing the car to stay in its lane.
• License Plate Recognition: Create an automated system that detects and reads vehicle license plates. Use computer vision to locate the plate in an image and OCR (Optical Character Recognition) to read characters. This has applications in parking systems or traffic enforcement.
• Sign Language Interpreter: Develop a vision-based interpreter for a sign language (e.g., ASL). Use a camera to capture hand gestures and a trained model (possibly a CNN or sequence model) to translate gestures into text. This improves accessibility for the hearing-impaired.
• Human Pose Estimation for Fitness: Build a system that estimates human pose joints from video. Use pose estimation frameworks (like OpenPose) to track body joints. Then, create an application that gives feedback on exercise form or counts repetitions (e.g., squats, push-ups) automatically.
• Augmented Reality Filters: Create fun AR filters (like those on social media) that overlay graphics on faces or objects. Use face landmark detection or image segmentation to place virtual hats, makeup effects, or accessories in real time. This project blends creativity with CV technology.
• Wildlife Species Classification: Use camera trap images to identify animal species. Train a classification model on labeled wildlife images (e.g., tiger vs deer). This is useful for conservation studies and shows how vision helps in environmental monitoring.
• Document Scanner and OCR: Develop a mobile app that scans a document (e.g., a page of text) using a camera and converts it to editable text. Use image processing to correct perspective and OCR libraries (or trained CNN) to recognize characters. The result is a searchable PDF or text file.
• Color-based Object Sorting System: Build a vision system that sorts objects by color. For example, a conveyor belt with red, green, blue items. Use color detection (HSV thresholds) to identify item color and control actuators to divert each object. This is a simple industrial vision application.
• Gesture-based Interface Control: Create a system where hand gestures control a computer or robot. Use a camera to detect hand shapes or movements (e.g., raised palm for “volume up”, fist for “volume down”). Train a CNN or use rule-based detection for gestures. It illustrates human-computer interaction with vision.

Robotics and Autonomous Systems Project Ideas

Robotics projects integrate hardware and AI algorithms to create intelligent machines. These ideas range from mobile robots to drones and autonomous vehicles, emphasizing control, perception, and decision-making.

• Self-Balancing Robot: Build a two-wheeled robot (like a Segway) that uses sensor feedback to balance itself upright. Implement control algorithms (PID or a neural network) using data from gyroscopes and accelerometers. It teaches feedback control and real-time decision-making.
• Line-Following Autonomous Car: Create a small robotic vehicle that follows a line on the ground. Use computer vision or infrared sensors to detect the line, and control the motors accordingly. This project helps students learn about sensor integration and control logic.
• Maze-Solving Robot: Design a robot that can navigate a maze or obstacle course. Use sensors (distance or vision) to detect walls and paths, and implement algorithms (like wall-following or A* search) for path planning. It combines mapping, planning, and real-time sensing.
• Robotic Arm Pick-and-Place: Program a robotic arm to pick up objects from one location and place them in another. Use computer vision to locate the object and compute coordinates. Implement inverse kinematics for arm movement. This project integrates robotics with vision and motion control.
• Drone Swarm Coordination: Develop algorithms for multiple drones to work together. Simulate a task like area coverage or target tracking with a group of drones. Use concepts from multi-agent systems and communication to coordinate actions, demonstrating swarm intelligence.
• Voice-Activated Robot Assistant: Build a mobile or stationary robot that responds to voice commands. Use speech recognition to interpret commands (e.g., “move forward”, “turn left”). The robot can then navigate or perform tasks accordingly, combining NLP and robotics.
• SLAM for Indoor Navigation: Implement Simultaneous Localization and Mapping (SLAM) on a wheeled robot. Use sensors (like LIDAR or a camera) to map an unknown environment and localize the robot within it. This allows the robot to build and use a map for autonomous navigation.
• Autonomous Wheelchair: Design a concept for a wheelchair that navigates indoors autonomously. Use depth sensors (e.g., Kinect) to detect obstacles and plan safe routes. Include user controls or voice commands for destinations. This project has strong social impact by aiding the differently-abled.

Internet of Things (IoT) and Edge AI Project Ideas

IoT and edge AI projects combine sensors and on-device intelligence. These ideas focus on building smart devices that can learn and act without relying fully on the cloud.

• Smart Home Automation System: Create an intelligent home control system that learns user habits. For example, it could predict when to turn lights on/off or adjust the thermostat based on occupancy and preferences. Use IoT sensors (motion, light, temperature) and train ML models on-device to optimize comfort and energy use.
• Wearable Health Monitoring: Develop a wearable device (e.g., using Arduino or Raspberry Pi) that monitors vital signs (heart rate, activity level) and uses AI to detect anomalies. For instance, alert if heart rate is irregular. This project teaches embedded systems and real-time data analysis for health.
• Smart Agriculture Irrigation: Build a system of soil moisture sensors and weather data to control irrigation. Use machine learning to predict when fields need water, automating pumps or valves. This IoT project combines sensor networks with predictive models to conserve water and maximize yield.
• Real-Time Object Detection on Embedded Device: Implement a lightweight object detection model that runs on a Raspberry Pi or smartphone. For example, a camera that counts cars or identifies people at the door. Use optimized models (MobileNet, TensorFlow Lite) to achieve fast, local inference without cloud.
• Environmental Air Quality Predictor: Use a network of low-cost air quality sensors to collect pollution data. Apply machine learning (possibly federated learning) to predict pollutant levels or identify patterns. Alert users when quality drops. This shows AI-driven IoT for public health.
• Voice-Controlled Smart Device: Program an IoT device (like a smart speaker) that responds to voice commands locally. Use an embedded voice recognition model to turn on lights or appliances. This emphasizes edge processing of voice commands for privacy.
• Factory Equipment Predictive Maintenance: Create a system of vibration or temperature sensors on industrial machines. Use AI to predict when maintenance is needed before failure. Train anomaly detection models on sensor streams. Such a project introduces students to industrial IoT concepts.

Reinforcement Learning Project Ideas

Reinforcement learning (RL) involves agents learning to make decisions through trial and reward. These projects often use simulations or games to teach RL algorithms like Q-learning or policy gradients.

• Game Playing Agent (e.g., Pac-Man): Build an RL agent that learns to play a classic game like Pac-Man or Snake. Use environments from OpenAI Gym or similar. The agent receives scores as rewards and learns policies (with Q-learning or deep Q-networks) to maximize the game score.
• Autonomous Car Simulator: Use a driving simulator (like CARLA or a custom simple simulator) to train a reinforcement learning agent to drive. The agent learns steering and throttle actions to stay on the road and avoid obstacles. This project connects RL with continuous action control.
• Stock Trading Agent: Create an RL-based trading bot that buys or sells stocks. Use historical stock data as an environment, define rewards based on profit/loss, and train a reinforcement learning algorithm to learn a trading strategy. It demonstrates risk/reward trade-offs.
• Elevator Scheduling Optimization: Model a building with multiple elevators and floor requests. Use reinforcement learning to optimize elevator movements to reduce wait time. The agent learns the best dispatch actions given current requests and elevator states.
• Recommendation System with RL: Implement a recommender that learns from user feedback. Treat content recommendations as actions and use clicks or ratings as rewards. An RL agent can learn to suggest items that maximize long-term user engagement.
• Maze Navigation Agent: Train an agent to navigate a maze or grid-world to reach a goal. Use tabular Q-learning or neural policy methods. This introduces value iteration concepts and pathfinding via learning.
• Resource Management Game: Develop a simple resource management simulation (like a city builder) where RL agents allocate resources. The agent learns policies to balance budgets and resources to achieve objectives. This can illustrate RL in economic environments.

Generative AI and Creativity Project Ideas

Generative AI focuses on creating new content. These projects involve generating text, images, music, and more, often using models like GANs, VAEs, or transformer networks.

• AI Music Composer: Build a model that composes music. Use recurrent neural networks or transformers trained on MIDI files of songs. The system can generate new melodies or harmonize existing tunes. This project merges deep learning with signal processing and creative arts.
• Poetry and Lyrics Generator: Create a model that generates poems or song lyrics. Train a language model on a corpus of poetry or lyrics. The AI can write original verses that mimic a certain style or theme. This demonstrates generative language modeling.
• Artistic Style Transfer: Develop an application that blends the style of one image with the content of another (e.g., make a photo look like a Van Gogh painting). Implement neural style transfer using convolutional networks. It’s a popular demo of how AI can create novel artwork.
• Text-to-Image Generation: Build a system that converts text descriptions into images. Use models like GANs (e.g., DALL·E style) or recent diffusion models. For example, input “sunset over mountains” and generate a plausible scene. This cutting-edge topic is very inspiring.
• AI-Driven Story Writer: Use a large language model to generate short stories or dialogues based on prompts. Fine-tune a transformer on a story dataset so it can continue narratives. Evaluate creativity and coherence, encouraging discussions on AI’s creative capabilities.
• GAN-based Face Swapper: Create an application that swaps faces in photos or videos using Generative Adversarial Networks. This involves detecting faces, aligning them, and using a trained GAN (like faceswap models) to morph one face onto another. It’s both technical and visually impressive.
• Voice Cloning and Synthesis: Develop a system that clones a person’s voice or generates speech in a new style. Use models like Tacotron or WaveNet. For example, train on a short sample and then generate arbitrary speech. This project intersects with audio processing and ethics.

AI in Healthcare and Bioinformatics Project Ideas

These projects apply AI to health data and biology, focusing on diagnosing diseases, drug discovery, and personalized medicine.

• Medical Image Diagnostic System: Build an AI tool to detect diseases from medical images (X-rays, MRIs, etc.). For example, train a CNN to identify pneumonia from chest X-rays. Use data augmentation and report accuracy metrics. This shows AI aiding radiologists in diagnostics.
• Patient Risk Prediction: Develop a model to predict patient outcomes, like hospital readmission or disease progression. Use EHR (electronic health records) data with patient history. Apply machine learning to identify at-risk patients early, improving healthcare management.
• Drug Discovery and Molecule Screening: Use generative models to propose new drug molecules. Train a model on known compounds and have it generate novel chemical structures with desired properties. This intersects AI with computational chemistry.
• Genomic Sequence Classification: Apply AI to DNA or protein sequences. For example, classify sequences as gene families or predict genetic diseases. Use techniques like CNNs on one-hot encoded sequences or transformer models for biological data.
• Virtual Health Assistant: Create a virtual chatbot for preliminary medical advice. The bot can ask symptom questions and provide general recommendations (or advise seeing a doctor). Focus on NLP models for medical dialogue, with a focus on privacy and accuracy.
• Smart Robotic Surgery Aid: Conceptualize a robotic assistant for surgery that can perform precise tasks (like suturing) under AI guidance. Use computer vision to track instruments and reinforcement learning for control. This complex project highlights AI in surgical robotics.

AI for Social Good and Environment Project Ideas

These ideas leverage AI to address social challenges, sustainability, and accessibility, aligning technology with humanitarian goals.

Personalized Adaptive Learning Platform: Create an AI-driven educational tool that adapts content to each student’s learning style. Use data on student performance to recommend lessons or exercises. This enhances learning outcomes, addressing educational inequality.

Wildfire Early Detection: Build an AI system that analyzes satellite or sensor data to detect signs of wildfires. Use image classification or anomaly detection on environmental data. Early alerts can help firefighters respond faster and reduce damage.

Smart Traffic Signal Control: Create an adaptive traffic light controller that minimizes congestion. Use reinforcement learning or optimization to adjust signal timings based on live traffic feeds. Improves urban traffic flow and reduces pollution.

Trash Sorting with Computer Vision: Develop a vision system that automatically sorts recyclable and non-recyclable waste on a conveyor belt. Use a camera and trained CNN to identify material types (plastic, metal, etc.). This promotes recycling and waste reduction.

Wildlife Population Monitoring: Use AI to count and classify animals from aerial or camera trap images. Train models to recognize species and estimate numbers in protected areas. Helps conservationists monitor ecosystems with less manual effort.

Flood Prediction and Alert System: Build a predictive model for flooding based on weather forecasts and terrain data. Use machine learning to learn from historical flood events. Issue alerts to residents in at-risk areas, potentially saving lives.

Miscellaneous AI Project Ideas

These ideas cover other emerging areas and interdisciplinary applications of AI that don’t fit neatly into the above categories.

• TinyML on Microcontrollers: Implement a simple machine learning model on a microcontroller (like Arduino) using TinyML. For example, train a small neural network to recognize a sound or gesture and run it on an embedded device. This shows AI in very resource-constrained environments.
• Blockchain and AI Integration: Explore combining blockchain with AI, for example creating a secure data marketplace for machine learning data. Or use ML to detect fraud in blockchain transactions. This cutting-edge project ties together two hot fields.
• Explainable AI Dashboard: Build a tool that explains AI model predictions in real time. For a given input, show which features influenced the decision (using SHAP values or LIME). For example, explain why a loan application was approved. This highlights AI transparency and trust.
• AI in Virtual Reality (VR) Environments: Develop an AI agent that interacts within a VR game or simulation. For example, an AI character that learns from player behavior to adjust difficulty or narrative. This merges AI decision-making with immersive experiences.
• Quantum Machine Learning Prototype: Design a small experiment of quantum-inspired machine learning. For example, simulate a quantum circuit for classification using Qiskit or PennyLane. While hardware is limited, it introduces students to the concept of quantum-enhanced AI.