Publication details
Chatbots Without Artificial Intelligence In Information Literacy Education Course: Design, Implementation, Evaluation
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Appeared in Conference without Proceedings |
| MU Faculty or unit | |
| Citation | |
| Description | This study describes how chatbots without artificial intelligence can be used educationally. Many publications focus on the development and application of chatbots in education with artificial intelligence. However, such an approach is unsuitable for "small languages" or minor courses. This paper analyses the design process of developing several chatbots created in Schatbot.me, a low user-barrier application. The analysed chatbots focus on the practical development of information literacy or its competence component. The study analyses the experience of using chatbots without artificial intelligence and offer implications for their further implementation. The study relies on two questionnaire surveys conducted on samples of respondents (students of Librarianship and Information Science) n=31 and n=79 from 2021 and 2022 who tested such chatbots dedicated to information literacy development. The open-ended questions were coded and then classified into subcategories. From their responses, principles and recommendations for the development of new chatbots were then formulated. The paper highlights the bottlenecks in implementing chatbots as learning objects (technical problems, user frustration, linguistic inadequacy, error detection in responses) and the strengths of such an approach (student motivation, practice, engagement in dialogue, modernity). The study provides information on a unique sample on a topic neglected in the literature. However, it has substantial practical implications for developing chatbots as supplements to e-learning activities, online courses or as a micro-learning tool. It fills the gap of simple, quickly formed conversational algorithms that do not require a professional team to create them or work with large datasets that may not always be available. Key recommendations include an emphasis on rigorous testing, working with story and anecdote, focusing on active elements of learning and competence (not just facts), and student choice of dialogue flow. |