One major challenge is clearly defining the problem statement and scope of the project. AI projects can often have very broad problem domains, so students need to carefully define the specific question they want to answer or task they want their model to perform. Narrowing the focus to a well-defined, manageable subset of the overall problem domain is key. Students should break down the problem, identify the key elements, consider what could realistically be accomplished within the timeframe and resource constraints of a capstone project. Getting feedback from instructors and peers on the proposed problem statement can help refine its clarity and scope.
Related to problem scoping is ensuring technical feasibility given available resources and skills. Students need to match their solution approach to the capabilities they and their team members possess. It’s common for early ideas to be overly ambitious and rely on advanced techniques still being learned. Regularly checking technical assumptions against abilities is important to avoid getting halfway into a project only to realize the desired approach will not work. Adjusting the vision to fit realistic technical boundaries helps improve chances of completion.
Sourcing and preparing appropriate data is another frequent roadblock. Many AI projects require large, specialized datasets which students may not have direct access to. Even publicly available data often needs preprocessing before being usable for modeling. This preprocessing step is frequently underestimated and can end up consuming significant project time if not planned for. Students should research potential data sources very early, get any needed approvals for access, and schedule data collection/preparation as part of the overall timeline. Starting model development before data is fully curated often stalls progress.
Related, ensuring representative and unbiased data can be more difficult without industry resources. Capstone projects conducted with small, convenient datasets run the risk of overfitting or unintentionally privileging majority groups. Getting input from diverse peer reviewers on the dataset and planned approach can help surface potential fairness issues. Synthetic data generation may also address limitations of real data access.
Model development and experimentation also takes longer than anticipated by many students. Choosing the right algorithms/techniques and hyperparameter tuning are iterative processes requiring multiple trial-and-error cycles. Sufficient time must be allotted for exploration, failure, and refinement. Starting work early allows for the inevitable ups and downs of research while still completing on schedule. Notebooks, documentation, and regular backup of works in progress further prevent wasted effort from technical mishaps.
Communication and coordination within student teams also poses frequent difficulties. Distributed workloads, conflicting schedules, and differing skillsets can cause delays without open communication and clear delegation of responsibilities. Establishing regular check-ins, standardized documentation practices, and backup points of contact helps diffuse potential roadblocks from interpersonal conflicts or individual underperformance. Maintaining synchronization across all contributions is essential for staying on track.
Presentation of research and results comprises another critical step where challenges often arise. Many students struggle to clearly convey technical concepts to non-specialist audiences in an organized manner. Practicing presentation material well in advance while getting peer and instructor feedback improves ability to defend work and showcase its relevance. Concise, visual summaries help audiences understand takeaways. Documentation should also be structured to demonstrate logical flow and conclusions to evaluators.
Common AI capstone project pitfalls center around unclear problem scoping, unrealistic ambitions, underestimating data preparation needs, lack of progressive feedback, insufficient experimentation time, poor team coordination, and weaknesses in communication of results. With careful upfront planning, establishing supportive peer review processes, regularly checking assumptions, and openness to iterative refinement, students can successfully navigate these challenges and produce polished work before deadline. Starting early and maintaining organization helps projects stay on track for successful completion.