AI Chatbots in Flipped Learning: Benefits and Challenges

The rise of AI chatbots, especially after ChatGPT, has sparked interest in their educational applications. While general-purpose chatbots have limitations, AI-based chatbots designed for specific learning purposes are gaining traction. Flipped learning, which involves pre-class learning and in-class activities, can be enhanced with AI chatbots providing 24/7 support. This review examines the design, benefits, and challenges of integrating AI chatbots into flipped learning environments.

This review followed the PRISMA guidelines, searching eleven electronic databases for relevant articles. The search string included terms related to conversational AI, chatbots, and flipped learning. Empirical studies, conference papers, and book chapters written in English were included. Data extraction focused on the design, benefits, and challenges of AI chatbot integration in flipped learning. Content analysis was used to identify emerging themes, with double-coding to ensure reliability.

The reviewed studies revealed varied approaches to chatbot design. Some researchers used ready-made applications like Replika, while others built custom chatbots using Python, Google Dialogflow, or IBM Watson Assistant. These chatbots were categorized as learning chatbots (facilitating out-of-class learning), assisting chatbots (providing course assistance), and mentoring chatbots (supporting self-regulated learning). Customization involved setting intents, entities, and dialogue relevant to specific courses.

Integrating AI chatbots into flipped learning offers several benefits. Immediate feedback helps students learn and achieve goals promptly. Increased interaction with learning content is facilitated through chatbot-guided activities. Improved class preparation results from chatbot reminders and support. Increased confidence is observed, especially in English-speaking courses. Data-driven teaching and learning are enabled through chatbot analytics, allowing for personalized learning experiences.

Despite the benefits, challenges exist. Limited technical functionality can restrict the complexity of tasks and responses. A lack of authenticity can make chatbots less appealing for complex questions. Insufficient student motivation can lead to inconsistent chatbot usage. These challenges highlight the need for ongoing improvements in chatbot design and implementation.

The review is limited by the small number of relevant empirical studies. The diverse subject areas of the reviewed studies make it difficult to draw broad conclusions. The reliance on self-reported data raises questions about the objective effectiveness of AI chatbots in flipped learning. Further research is needed to address these limitations.

Future research should focus on developing comprehensive Q&A databases to enhance chatbot functionality. Involving teachers in chatbot design can ensure authenticity and relevance. Strategies to improve student motivation, such as allocating scores or using Self-determination Theory, should be explored. Embedding emojis and emotional reactions can make chatbots more engaging.

The integration of AI chatbots into flipped learning presents both opportunities and challenges. While chatbots can enhance student learning and engagement, careful design and implementation are crucial. Future research and development should focus on addressing the identified limitations and challenges to fully realize the potential of AI chatbots in education. As AI technology advances, its role in flipped learning is poised to expand, transforming the educational landscape.