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Architectures and Innovations in AI-Driven Humour Generation: A Comprehensive Review

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  • N. Uday Bhaskar

Abstract

Humour, a uniquely human blend of creativity, emotion, and culture, poses a formidable challenge for artificial intelligence (AI). This review synthesizes key architectures, algorithms, and models driving AI-based humour generation—from early rule-based systems to advanced large language and multimodal frameworks. Classical humour theories of incongruity, superiority, and relief are examined in relation to contemporary techniques using Natural Language Processing (NLP), Machine Learning (ML), Generative Adversarial Networks (GANs), and Transformer architectures. Case studies of ChatGPT-4, Gemini 1.5, HumorGAN, Woebot 2.0, and Inworld AI demonstrate real-world applications across conversation, marketing, mental health, and entertainment. Ethical issues such as cultural bias, inclusivity, and creativity–coherence balance are analyzed alongside frameworks like EthicalHumorNet (2024), FunBench (2025), IEEE P7003, and OECD AI Ethics Review (2025). Looking forward, the paper envisions Responsible Artificial Wit (RAW)—AI that generates humour ethically, empathetically, and cross-culturally—marking a shift from computational imitation to socially intelligent creativity in affective computing.

Suggested Citation

  • N. Uday Bhaskar, 2025. "Architectures and Innovations in AI-Driven Humour Generation: A Comprehensive Review," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(5), pages 359-378, October.
    • Handle: RePEc:jbh:ijsrcs:v11:y2025:i5:id:1746
    DOI: 10.32628/CSEIT251117237
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT251117237
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