AI Thesis Topic Generator

Avoid broad terms like "Artificial Intelligence"; try "Deep learning applications in medical image diagnosis".

Provide a concrete setting, e.g., "Security analysis of blockchain-based supply chain provenance".

Clarify the research subject, e.g., "Optimizing LSTM-based stock price prediction models".

Mention techniques where relevant, e.g., "Improving CNN-based image classification".

Suggestions are generated from broad academic knowledge and should be adapted to your program and advisor guidance.

• Discuss with your advisor to ensure program alignment
• Conduct a literature review to assess the state of the art
• Evaluate your skills and available data/resources
• Check timeline feasibility for your degree requirements

Use AI topics as a starting point, then refine and personalize.

• Input length: keep it between 5–500 characters
• Connectivity: check your network and retry
• Rate limit: up to 5 requests/minute—wait and try again
• Server load: try again during off-peak hours
• Content policy: avoid disallowed/sensitive content

If failures persist, rephrase or simplify keywords.

• Undergraduate: 3–6 months; prioritize applied/verification studies
• Master’s: 1–2 years; include moderate novelty and evaluation
• PhD: 3–5 years; requires substantial theoretical or methodological advances

• Data: do you have access to adequate datasets?
• Skills: are the required methods within your abilities?
• Compute: do you have needed hardware or cloud budget?
• Collaboration: will you need cross‑disciplinary or industry support?

Aim for challenging but time‑bounded topics you can finish.

• Narrow the subject: e.g., from "e‑commerce" to "B2C e‑commerce"
• Constrain the scenario: from "recommender systems" to "music recommendation"
• Focus the problem: from "cybersecurity" to "DDoS mitigation"
• Add technical bounds: from "image recognition" to "CNNs for medical imaging"

• Extend to adjacent domains
• Add comparative baselines or methods
• Introduce new evaluation dimensions (performance, security, fairness)
• Combine with emerging techniques

• Survey the last 3–5 years of work
• Monitor top conferences and journals
• Use Google Scholar, IEEE Xplore, ACM DL, PubMed, etc.
• Read surveys for open problems/future work

• Technique: improve algorithms or propose new ones
• Application: apply mature methods to new domains
• Cross‑disciplinary: blend methods across fields
• Gap analysis: identify unresolved limitations

Ensure your idea isn’t already fully addressed in the literature.

• Focus: applied projects, implementation, validation
• Example: Design and build an online shopping system
• Requirements: complete features, feasible scope
• Novelty: moderate; emphasize core skills

• Focus: method improvement, optimization, comparisons
• Example: Vehicle routing optimization with improved GA
• Requirements: some novelty, solid experiments
• Novelty: clear incremental contribution

• Focus: theoretical innovation, major advances
• Example: New GNN theory/methods for complex networks
• Requirements: originality, depth, broader impact
• Novelty: fills a gap or opens a new direction