4 Ways For Using Chatbots For eLearning

Created by Joseph Weizenbaum at MIT in 1966, ELIZA was one of the earliest chatbot programs (Weizenbaum, 1966). ELIZA could mimic human-like responses by reflecting user inputs as questions. Another early example of a chatbot was PARRY, implemented in 1972 by psychiatrist Kenneth Colby at Stanford University (Colby, 1981).

Chatbots are part of the digital discovery process that tests the current idea of communication through audio and scriptural methods, providing new and exciting opportunities for eLearning platforms. Looking through the lens of digital surrealism, educational chatbots are used in dialogue systems for various practical purposes, developing new directions of study that make it possible to find relevant information in real time. The implications of the research findings for policymakers and researchers are extensive, shaping the future integration of chatbots in education. The findings emphasize the need to establish guidelines and regulations ensuring the ethical development and deployment of AI chatbots in education.

Educational Robots Market Higher education segment dominated the market with 35% of the total revenue share, in 2022

Pounce reduced the university’s summer melt rate by 21%, leading to higher enrollment and retention rates for STEM students. There’s a lot of fascinating research in the area of human-robot collaboration and human-robot teams. Although chatbots can provide information, they should not act as a substitute for, instead of spurring the development of students’ critical thinking and analytical skills. Universities need to emphasize the importance of independent research, critical evaluation, and synthesis of knowledge. Chatbots can facilitate online discussions, group projects, and collaborative learning experiences, allowing students to engage with peers and share ideas, fostering community and active participation.

Only one study pointed to high usefulness and subjective satisfaction (Lee et al., 2020), while the others reported low to moderate subjective satisfaction (Table 13). For instance, the chatbot presented in (Lee et al., 2020) aims to increase learning effectiveness by allowing students to ask questions related to the course materials. It turned out that most of the participants agreed that the chatbot is a valuable educational tool that facilitates real-time problem solving and provides a quick recap on course material. The study mentioned in (Mendez et al., 2020) conducted two focus groups to evaluate the efficacy of chatbot used for academic advising. While students were largely satisfied with the answers given by the chatbot, they thought it lacked personalization and the human touch of real academic advisors. Finally, the chatbot discussed by (Verleger & Pembridge, 2018) was built upon a Q&A database related to a programming course.

Share this article

With a mobile learning chatbot, students can hone their linguistic abilities, learn new skills, study for tests, and expand their understanding of their chosen field. Because most mobile learning chatbots are designed to communicate only in one language, they may not be as useful to students who do not already speak that language. Chatbots should be made available in several different languages to appeal to a larger range of students and consider the intended audience’s linguistic diversity. Chatbots are computer programs designed to simulate conversation with human users, and they have gained immense popularity in recent years. In education, they serve as virtual assistants that provide students and educators with immediate access to information, resources, and support.

However, after OpenAI clarified the data privacy issues with Italian data protection authority, ChatGPT returned to Italy. To avoid cheating on school homework and assignments, ChatGPT was also blocked in all New York school devices and networks so that students and teachers could no longer access 2023; Li et al., 2023). These examples highlight the lack of readiness to embrace recently developed AI tools. There are numerous concerns that must be addressed in order to gain broader acceptance and understanding. Example flow diagrams from Textit for the design and development of the chatbot are represented in Fig.

Creative writing brainstorm

6, the illustration describes changes in each group (EC and CT) pre and post-intervention. First, teamwork showed an increasing trend for EC, whereas CT showed slight changes pre and post-intervention. At last, we could have missed articles that report an educational chatbot that could not be found in the selected search databases. To deal with this risk, we searched manually to identify significant work beyond the articles we found in the search databases.

In other cases, the teaching agent started the conversation by asking students to reflect on past learning (Song et al., 2017). Other studies discussed a scenario-based approach to teaching with teaching agents (Latham et al., 2011; D’mello & Graesser, 2013). The teaching agent simply mimics a tutor by presenting scenarios to be discussed with students. In other studies, the teaching agent emulates a teacher conducting a formative assessment by evaluating students’ knowledge with multiple-choice questions (Rodrigo et al., 2012; Griol et al., 2014; Mellado-Silva et al., 2020; Wambsganss et al., 2020). Setting up a mobile learning chatbot with the LMS is hassle-free with Instancy’s plug-and-play compatibility. Administrators can effortlessly integrate the chatbot with their existing LMS.

This means that Google Bard is more likely to be up-to-date on current events, while ChatGPT is more likely to be accurate in its responses to factual questions (AlZubi et al., 2022; Rahaman et al., 2023; Rudolph et al., 2023). Furthermore, the feedbacks also justified why other variables such as the need for cognition, perception of learning, creativity, self-efficacy, and motivational belief did not show significant differences. For instance, both groups portrayed high self-realization of their value as a team member at the end of the course, and it was deduced that their motivational belief was influenced by higher self-efficacy and intrinsic value. Next, in both groups, creativity was overshadowed by post-intervention teamwork significance. Therefore, we conclude that ECs significantly impact learning performance and teamwork, but affective-motivational improvement may be overshadowed by the homogenous learning process for both groups. Furthermore, in regard to problems faced, it was observed that in the EC group, the perception transformed from collaboration issues towards communicative issues, whereas it was the opposite for the CT group.

According to Adamopoulou and Moussiades (2020), it is impossible to categorize chatbots due to their diversity; nevertheless, specific attributes can be predetermined to guide design and development goals. For example, in this study, the rule-based approach using the if-else technique (Khan et al., 2019) was applied to design the EC. The rule-based chatbot only responds to the rules and keywords programmed (Sandoval, 2018), and therefore designing EC needs anticipation on what the students may inquire about (Chete & Daudu, 2020). Furthermore, a designer should also consider chatbot’s capabilities for natural language conversation and how it can aid instructors, especially in repetitive and low cognitive level tasks such as answering FAQs (Garcia Brustenga et al., 2018). As mentioned previously, the goal can be purely administrative (Chocarro et al., 2021) or pedagogical (Sandoval, 2018).

Consequently, it has prompted a significant surge in research, aiming to explore the impact of chatbots on education. Subsequently, we delve into the methodology, encompassing aspects such as research questions, the search process, inclusion and exclusion criteria, as well as the data extraction strategy. Moving on, we present a comprehensive analysis of the results in the subsequent section. Finally, we conclude by addressing the limitations encountered during the study and offering insights into potential future research directions. Moreover, individual personality traits such as motivation have also been found to influence creativity (van Knippenberg & Hirst, 2020) which indirectly influenced the need for cognition (Pan et al., 2020). Nevertheless, these nonsignificant findings may have some interesting contribution as it implies that project-based learning tends to improve these personality-based learning outcomes.

The first step is determining what chatbot platform best serves the institution’s needs. Integrating the chatbot into the current educational ecosystem requires customization and integration. Chatbots can benefit greatly from regular training and supervision to function efficiently. Finally, protecting students’ personal information requires a focus on data privacy and security. It is crucial to be aware of the potential limitations and handle ethical, privacy, and technical concerns, yet mobile learning chatbots offer considerable benefits in education and training. The potential of mobile learning chatbots to transform education and provide students worldwide with greater agency can be realized if institutions consider these obstacles and take the necessary precautions.

Study Limitations

Moreover, it has been found that teaching agents use various techniques to engage students. In this approach, the agent acts as a novice and asks students to guide them along a learning route. Rather than directly contributing to the learning process, motivational agents serve as companions to students and encourage positive behavior and learning (Baylor, 2011). Using chatbots with VR/AR creates exciting new opportunities for hands-on, interactive education.

Bureau of Labor Statistics, many STEM occupations are projected to grow at a much faster rate than non-STEM jobs, and they typically offer higher median wages. E-learning and chatbots play a crucial role in preparing students for these in-demand jobs by providing them with the knowledge and skills required to succeed. One challenge is ensuring that all students have access to high-quality e-learning platforms and chatbots.

• Chatbots provide instantaneous assistance, which is unavailable in some traditional educational situations.
• They might be combined with new technologies like VR and AR to give students a hands-on, interactive experience with what they’re studying.
• The authors declare that this research paper did not receive any funding from external organizations.
• Teaching agents play the role of human teachers and can present instructions, illustrate examples, ask questions (Wambsganss et al., 2020), and provide immediate feedback (Kulik & Fletcher, 2016).
• Chatbots can provide virtual tutoring and mentoring services, guiding students through coursework, assignments, and career advice.

They also act as study companions, offering explanations and clarifications on various subjects. They can be used for self-quizzing to reinforce knowledge and prepare for exams. Furthermore, these chatbots facilitate flexible personalized learning, tailoring their teaching strategies to suit each student’s unique needs. Their interactive and conversational nature enhances student engagement and motivation, making learning more enjoyable and personalized. Also, AI chatbots contribute to skills development by suggesting syntactic and grammatical corrections to enhance writing skills, providing problem-solving guidance, and facilitating group discussions and debates with real-time feedback.

Read more about Chatbot in the E-Learning here.

• Beyond gender and form of the bot, the survey reveals many open questions in the growing field of human-robot interaction (HRI).
• Businesses must pick a chatbot platform that works with their existing systems and supports their mission.
• Chatbots can now be accessed from any mobile learning platform, including Learning Management Systems (LMS) and e-learning platforms.
• A few crucial measures must be addressed before mobile learning chatbots can be used effectively in the classroom.