Exploring the pedagogical uses of AI chatbots
At the end of a company training session, employees might rush through feedback forms, giving little or no helpful criticism. It can ask specific questions like, “Was the Data Analytics Concepts module too fast-paced?” or “Would you like more visual aids in the next session?” This interactive way can get more detailed and actionable feedback. In the traditional educational environment, if you have a question, you need to wait for teacher availability or office hours.
As the use of technology in the classroom grows, so do questions of ethics and privacy. Data privacy and security concerns have been raised concerning mobile learning chatbots, which collect and process user data to customize the learning experience. Protecting students’ privacy requires strict privacy protections and adherence to data protection standards. Only four (11.11%) articles used chatbots that engage in user-driven conversations where the user controls the conversation and the chatbot does not have a premade response. For example, the authors in (Fryer et al., 2017) used Cleverbot, a chatbot designed to learn from its past conversations with humans. User-driven chatbots fit language learning as students may benefit from an unguided conversation.
Potential risks when using AI
The number of choices and possible outputs determine the complexity of the chatbot where some chatbots may have simple interaction that requires them to register their groups (Fig. 2) or much more complex interaction for peer-to-peer assessment (Fig. 3). Therefore, it was hypothesized that using ECs could improve learning outcomes, and a quasi-experimental design comparing EC and traditional (CT) groups were facilitated, as suggested by Wang et al. (2021), to answer the following research questions. Nevertheless, enhancing such skills is often time-consuming, and teachers are usually not mentally prepared to take up a designer’s (Kim, 2021) or programmer’s role. The solution may be situated in developing code-free chatbots (Luo & Gonda, 2019), especially via MIM (Smutny & Schreiberova, 2020).
- Criteria were determined to ensure the studies chosen are relevant to the research question (content, timeline) and maintain a certain level of quality (literature type) and consistency (language, subject area).
- As they gain skills in areas like software development, data science, artificial intelligence, and cybersecurity through online courses and chatbot-assisted learning, they become qualified candidates for tech-related job opportunities.
- Organizations may now provide more efficient and interesting educational opportunities for their employees thanks to mobile learning chatbots.
- First, teamwork showed an increasing trend for EC, whereas CT showed slight changes pre and post-intervention.
According to Pintrich et al. (1993), self-efficacy and intrinsic value strongly correlate with task value (Eccles & Wigfield, 2002), such as interest, enjoyment, and usefulness. Ensuing, the researcher also considered creative self-efficacy, defined as the students’ belief in producing creative outcomes (Brockhus et al., 2014). Prior research has not mentioned creativity as a learning outcome in EC studies. However, according to Pan et al. (2020), there is a positive relationship between creativity and the need for cognition as it also reflects individual innovation behavior.
Using EdTech Tools to Enhance STEM Learning
Two recent articles in the journal Nature described its application to weather forecasting. We encourage you to organize your colleagues to complete these modules together. Consider how you might adapt, remix, or enhance these modules for your own needs. If you have any questions, contact us at This guide was created by Stanford Teaching Commons and is licensed under Creative Commons BY-NC-SA 4.0 (attribution, non-commercial, share-alike). In conversations with other people, we routinely ask for clarifying details, repeat ideas in different ways, allow a conversation to go in unexpected directions, and guide others back to the topic at hand.
It is evident that chatbot technology has a significant impact on overall learning outcomes. Specifically, chatbots have demonstrated significant enhancements in learning achievement, explicit reasoning, and knowledge retention. The integration of chatbots in education offers benefits such as immediate assistance, quick access to information, enhanced learning outcomes, and improved educational experiences.
The Future Of eLearning With Chatbot Technology: How To Use Chatbots For eLearning
Although this technology is currently in the prototype phase, the Hewitt‘s Foundation has organized a competition between the most famous essay scorers. According to the report written by Huyen Nguyen and Lucio Dery, from the Department of Computer Science at Stanford University, the winning app had 81% correlation with the human grader. Hands-on experience using a chatbot can help you to better understand the capabilities and limitations of these tools. Try completing some of the following tasks, or the example educational use cases above, to practice using a chatbot. The ability to transfer skills and knowledge that you learned to a new situation involves abstract thinking, problem-solving, and self-awareness.
Interestingly, the only peer agent that allowed for a free-style conversation was the one described in (Fryer et al., 2017), which could be helpful in the context of learning a language. The teaching agents presented in the different studies used various approaches. For instance, some teaching agents recommended tutorials to students based upon learning styles (Redondo-Hernández & Pérez-Marín, 2011), students’ historical learning (Coronado et al., 2018), and pattern matching (Ondáš et al., 2019). In some cases, the teaching agent started the conversation by asking the students to watch educational videos (Qin et al., 2020) followed by a discussion about the videos.
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Studies that used questionnaires as a form of evaluation assessed subjective satisfaction, perceived usefulness, and perceived usability, apart from one study that assessed perceived learning (Table 11). Assessing students’ perception of learning and usability is expected as questionnaires ultimately assess participants’ subjective opinions, and thus, they don’t objectively measure metrics such as students’ learning. By far, the majority (20; 55.55%) of the presented chatbots play the role of a teaching agent, while 13 studies (36.11%) discussed chatbots that are peer agents. Only two studies used chatbots as teachable agents, and two studies used them as motivational agents.
Among them, ChatGPT and Google Bard are among the most profound AI-powered chatbots. It was first announced in November 2022 and is available to the general public. ChatGPT’s rival Google Bard chatbot, developed by Google AI, was first announced in May 2023. Both Google Bard and ChatGPT are sizable language model chatbots that undergo training on extensive datasets of text and code. They possess the ability to generate text, create diverse creative content, and provide informative answers to questions, although their accuracy may not always be perfect. The key difference is that Google Bard is trained on a dataset that includes text from the internet, while ChatGPT is trained on a dataset that includes text from books and articles.
Chatbots have been utilized in education as conversational pedagogical agents since the early 1970s (Laurillard, 2013). Pedagogical agents, also known as intelligent tutoring systems, are virtual characters that guide users in learning environments (Seel, 2011). They are characterized by engaging learners in a dialog-based conversation using AI (Gulz et al., 2011).
Nevertheless, the manual search did not result in any articles that are not already found in the searched databases. Another interesting study was the one presented in (Law et al., 2020), where the authors explored how fourth and fifth-grade students interacted with a chatbot to teach it about several topics such as science and history. The students appreciated that the robot was attentive, curious, and eager to learn. As an example of an evaluation study, the researchers in (Ruan et al., 2019) assessed students’ reactions and behavior while using ‘BookBuddy,’ a chatbot that helps students read books. The researchers recorded the facial expressions of the participants using webcams. It turned out that the students were engaged more than half of the time while using BookBuddy.
Why Use Chatbots In Learning Management Systems?
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