Chatbots Made Easy: A Practical Workshop on Generative AI

Mochammad Rizqi Aullia, Muhammad Daffa Fadillah, Khoirul Umam, Dimas Septiana, Alfiana Rahmawati Gunawan, Sitti Nurbaya Ambo, Nurvelly Rosanti, Yana Ardhani, Jumail Jumail

Abstract


Chatbots powered by Generative AI have become a rapidly evolving technological innovation, enabling users to interact with AI-based systems in a more natural way. However, the implementation of these chatbots still faces many challenges, especially for beginners who lack a deep technical background. This study aims to develop a more accessible and understandable approach for those new to Generative AI. Through webinars and workshops, participants were introduced to the fundamentals of language models, the use of tools such as Google Colab, as well as various challenges and opportunities in AI chatbot development. Some of the main obstacles encountered included complex materials, limited access to data and computational resources, and varying levels of understanding among participants. Nevertheless, these activities provided beginners with the opportunity to grasp the basics of Generative AI and even develop their own chatbots. With a more practical and inclusive approach, this study contributes to introducing Generative AI-based chatbot technology to a broader audience. Evaluation results showed positive feedback from 77 participants from diverse backgrounds, with 51.9% stating they were very satisfied, 37.7% satisfied, and 10.4% neutral. Additionally, Pre-Test and Post-Test results indicated an improvement in participants' understanding of building simple chatbots. This initiative successfully provided a strong foundation for beginners to enter the field of Artificial Intelligence, particularly Generative AI

Keywords


Generative AI, Chatbot, Language Model, NLP, Google Colab, Machine Learning, Workshop, Artificial Intelligence Technology.

References


Chang, D., Lin, M., Hajian, S., & Wang, Q. (2023). Educational design principles of using AI chatbot that supports self-regulated learning in education: Goal setting, feedback, and personalization. Sustainability. https://doi.org/10.3390/su151712921

Allen, M., Naeem, U., & Gill, S. (2024). Q-Module-Bot: A generative AI-based question and answer bot for module teaching support. IEEE Transactions on Education, 67, 793-802. https://doi.org/10.1109/TE.2024.3435427

Nambiar, J., & Sreedevi, A. (2023). Orchestrating consensus strategies to counter AI hallucination in generative chatbots. 2023 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), 148-152. https://doi.org/10.1109/CCEM60455.2023.00030

Zhang, J., Oh, Y., Lange, P., Yu, Z., & Fukuoka, Y. (2020). Artificial intelligence chatbot behavior change model for designing artificial intelligence chatbots to promote physical activity and a healthy diet: Viewpoint. Journal of Medical Internet Research, 22. https://doi.org/10.2196/22845

Aprilia, R. (2024). SISTEM TANYA JAWAB ILMU KEISLAMAN DENGAN MODEL LARGE LANGUAGE MODELS. SISTEM TANYA JAWAB ILMU KEISLAMAN DENGAN MODEL LARGE LANGUAGE MODELS, 7(1), 80-87.

Fathurohman, A. (2021). Machine learning untuk pendidikan: Mengapa dan bagaimana. Jurnal Informatika Dan Teknologi Komputer (JITEK), 1(3), 57-62. https://doi.org/10.55606/JITEK.V1I3.306

Ulfa, M. (2021). Penerapan jaringan syaraf tiruan prediksi kebutuhan alat lampu penerangan jalan umum (LPJU) dengan metode backpropagation. Jurnal Ilmiah Abdi Ilmu, 14(1), 59-65. https://journal.pancabudi.ac.id

Sudrajat, D., Permatasari, R. D., Wijaya, I. M. S., Setyawan, A. E., & Rahayu, N. (2023). Pemanfaatan kecerdasan buatan sebagai upaya pengembangan media pembelajaran berbasis multimedia. Jurnal Kridatama Sains dan Teknologi, 5(02), 590-598. https://doi.org/10.53863/KST.V5I02.999


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DOI: 10.55824/jpm.v4i3.548

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