I’m nearing the end of my AI course for my Computer Science degree, and I need to write a 20-page research paper focusing on a specific AI topic. I’m particularly interested in areas like pathfinding algorithms after hearing about their use in games like Super Mario, but I’m struggling to choose a subject that I can write quickly since I only have a week left.
The paper needs to be a technical discussion, and since most of my previous writing has been for English classes, I’m unsure how to tackle this style. If I include programming in the paper, I can cut it down to just 8 pages, but I’ll need a results section to highlight my coding insights. Is my understanding correct?
What AI topics do you think would be straightforward to research and code, providing meaningful content for the paper?
Try a basic genetic algorithm for the traveling salesman problem. I did this in my final semester - coded it in 2-3 days and got tons of research material. You can mess around with different selection methods, crossover techniques, and mutation rates. Each tweak gives you measurable results to analyze. You’ll get concrete data like convergence rates, solution quality across generations, and how population size affects things. The algorithm’s pretty intuitive, so explaining your methodology is way easier than dealing with complex neural networks. Start with 10-15 cities, use basic tournament selection and order crossover, then track how parameter changes affect performance. Your results section can show fitness graphs generation by generation plus visualizations of the final tours. Technical papers aren’t like English essays - you’re documenting what you actually found, not making arguments. Just focus on what you observed, measured, and concluded from your experiments. The coding gives you that 8-page length you need, and the algorithm itself has plenty of technical depth to dig into.
With your tight deadline, skip pathfinding and go with machine learning classification instead. I did something similar - built a basic decision tree and compared it to k-nearest neighbors using Iris or Wine Quality datasets. You can knock out both algorithms in under two days, then spend the rest analyzing performance differences. Your results section writes itself: accuracy rates, confusion matrices, training times, memory usage - all solid technical content. Start with scikit-learn for baseline results, then code simplified versions from scratch. Gives you tons to discuss about optimization trade-offs and complexity. For writing style - structure it as problem statement, methodology, implementation, results, analysis. Focus on objective observations, not persuasive arguments like English papers. Load it up with charts and performance metrics. Alternative option: implement k-means clustering. Easy to visualize, looks great on paper, and shows clear algorithmic behavior. The math’s straightforward enough to explain thoroughly within your page limit.
A* pathfinding is perfect for your situation. I’ve built it dozens of times in production - not just games, but route optimization tools too.
What’s great about A* is you can code a working version in a day, then dive into analyzing different heuristics. Manhattan distance vs Euclidean vs custom heuristics gives you tons to write about.
Another quick win: build a basic neural network from scratch for image classification. Use MNIST - it’s small, well documented, and you’ll get results fast. Compare your implementation against existing frameworks and discuss the performance gaps.
Want something more current? Try sentiment analysis with different approaches. Start with bag of words, then build a basic RNN. The contrast between traditional NLP and modern approaches practically writes itself.
For the technical writing - stick to methodology, results, and analysis. Your results section needs performance metrics, runtime comparisons, or accuracy measurements. Include graphs and tables.
Avoid reinforcement learning or deep learning architectures. They sound impressive but take forever to train and debug, especially with your timeline.
Go with A* pathfinding. You can implement it quickly, test it on different maze types, and have solid performance data to analyze.