Exploring the pedagogical uses of AI chatbots
Likewise, it was deemed necessary due to the nature of the project, which involves design. Lastly, teamwork perception was defined as students’ perception of how well they performed as a team to achieve their learning goals. According to Hadjielias et al. (2021), the cognitive state of teams involved in digital innovations is usually affected by the task involved within the innovation stages. Future studies should explore chatbot localization, where a chatbot is customized based on the culture and context it is used in. Moreover, researchers should explore devising frameworks for designing and developing educational chatbots to guide educators to build usable and effective chatbots.
The app was created by the Polish inventor Piotr Wozniak and promoted by the SuperMemo company. Users should be cautious about the information generated by chatbots and not rely solely on them as sources of information. They should critically evaluate and fact-check the responses to prevent the spread of misinformation or disinformation. Users must use chatbots in a manner that respects the rights and dignity of others. They should not be used for malicious purposes, harassment, hate speech, or any activity that violates applicable laws or regulations. Chatbots’ responses can vary in accuracy, and there is a risk of conveying incorrect or biased information.
Universities must ensure quality control mechanisms to verify the accuracy and reliability of the AI-generated content. Special care must be taken in situations where faulty information could be dangerous, such as in chemistry laboratory experiments, using tools, or constructing mechanical devices or structures. “I also gave it the challenge of coming up with creative ideas for foods in my fridge based on an original photo (it identified the items correctly, though the creative recipe suggestions were mildly horrifying).” We offer this activity for you to self-assess and reflect on what you learned in this module.
One-way user-driven chatbots use machine learning to understand what the user is saying (Dutta, 2017), and the responses are selected from a set of premade answers. In contrast, two-way user-driven chatbots build accurate answers word by word to users (Winkler & Söllner, 2018). Such chatbots can learn from previous user input in similar contexts (De Angeli & Brahnam, 2008). Like any other technology, mobile learning chatbots have advantages and disadvantages.
Learning Content Management & eLearning Authoring Tools
For instance, Winkler and Söllner (2018) classified the chatbots as flow or AI-based, while Cunningham-Nelson et al. (2019) categorized the chatbots as machine-learning-based or dataset-based. In this study, we carefully look at the interaction style in terms of who is in control of the conversation, i.e., the chatbot or the user. Keep reading to learn more about the benefits of mobile learning chatbots, as well as the obstacles and caveats that must be considered before adopting this technology. Chatbots allow learners to ask questions and get instant answers in a conversational format. This participation helps students learn more deeply about the topic while developing their analytical and problem-solving abilities. In the modern age, chatbots are turned out to be the most innovative solution in bridging the gap between technology and education.
But with chatbots, reminders aren’t just automated alerts; they’re smart, interactive nudges. They have to learn about the company’s rules, computer tools, HR processes, and other things. It can explain what a course subject is, how to turn in a task, and even what jargon means.
Notable examples are explained in (Rodrigo et al., 2012; Griol et al., 2014), where the authors presented a chatbot that asks students questions and provides them with options to choose from. Other authors, such as (Daud et al., 2020), used a slightly different approach where the chatbot guides the learners to select the topic they would like to learn. Subsequently, the assessment of specific topics is presented where the user is expected to fill out values, and the chatbot responds with feedback. The level of the assessment becomes more challenging as the student makes progress. A slightly different interaction is explained in (Winkler et al., 2020), where the chatbot challenges the students with a question.
Learners may effortlessly access various knowledge and learning tools whenever they want, whether during a commute, lunch break, or even before bedtime. Below, we’ll take a closer look at the advantages, practical applications, techniques for implementation, difficulties, and potential of mobile learning chatbots in the classroom. Put on your seatbelts because you’re about to go on a ride that will forever alter our approach to education and training. The integration of e-learning and chatbot technology into STEM education holds enormous potential for wealth creation.
AI as teammate
The chatbot is designed to communicate with the LMS in plain English, eliminating the need for complex configurations or technical expertise. This user-friendly setup process means educational institutions and organizations can quickly deploy the chatbot and begin empowering learners with conversational access to learning resources. Education is only one field that has started to see the effects of virtual and augmented reality. Educators can create extremely dynamic and realistic learning experiences by combining the capabilities of mobile learning chatbots with these immersive technologies. Another topic is integrating mobile learning chatbots with cutting-edge technology like VR and AR.
Furthermore, ECs were also found to increase autonomous learning skills and tend to reduce the need for face-to-face interaction between instructors and students (Kumar & Silva, 2020; Yin et al., 2021). Conversely, this is an added advantage for online learning during the onset of the pandemic. Likewise, ECs can also be used purely for administrative purposes, such as delivering notices, reminders, notifications, and data management support (Chocarro et al., 2021). Moreover, it can be a platform to provide standard information such as rubrics, learning resources, and contents (Cunningham-Nelson et al., 2019). According to Meyer von Wolff et al (2020), chatbots are a suitable instructional tool for higher education and student are acceptive towards its application.
1 Research questions
Concerning their interaction style, the conversation with chatbots can be chatbot or user-driven (Følstad et al., 2018). Chatbot-driven conversations are scripted and best represented as linear flows with a limited number of branches that rely upon acceptable user answers (Budiu, 2018). When the user provides answers compatible with the flow, the interaction feels smooth. Chatbots are well-suited to learners who prefer self-paced and on-the-go education because of their capacity to offer knowledge in bite-sized, interactive formats.
- In today’s competitive market of technology, tech companies have become an important part when it comes to developing solutions with the latest technology to refine education.
- To summarize, incorporating AI chatbots in education brings personalized learning for students and time efficiency for educators.
- But with chatbots, reminders aren’t just automated alerts; they’re smart, interactive nudges.
- Education and training have evolved significantly, with technology playing a transformative role in enhancing learning experiences.
- Furthermore, there are also limited studies in strategies that can be used to improvise ECs role as an engaging pedagogical communication agent (Chaves & Gerosa, 2021).
As you begin to explore, think about what you already know and the opinions you may already hold about the educational aspects of AI chatbots. This metacognitive exercise can help you identify what you want to explore and what you already understand. Making connections to what you already know can deepen your learning and support your engagement with these modules (Santascoy, 2021). For these and other geopolitical reasons, ChatGPT is banned in countries with strict internet censorship policies, like North Korea, Iran, Syria, Russia, and China. Several nations prohibited the usage of the application due to privacy apprehensions.
Educators can improve their pedagogy by leveraging AI chatbots to augment their instruction and offer personalized support to students. By customizing educational content and generating prompts for open-ended questions aligned with specific learning objectives, teachers can cater to individual student needs and enhance the learning experience. Additionally, educators can use AI chatbots to create tailored learning materials and activities to accommodate students’ unique interests and learning styles.
This integration ensures learners can access the entire LMS features and resources through the chatbot. Learners can enroll in courses, access their learning progress, view grades, and engage with social learning features—all through a simple conversation with the chatbot. Consider a medical student who uses an AR-enabled mobile learning chatbot to simulate operating on a patient. The chatbot might guide the learner through the process while they practice with simulated patients and instruments. This incorporation also gives students a secure and managed environment to hone their practical abilities.
Finally, researchers should explore EUD tools that allow non-programmer educators to design and develop educational chatbots to facilitate the development of educational chatbots. Adopting EUD tools to build chatbots would accelerate the adoption of the technology in various fields. While the identified limitations are relevant, this study identifies limitations from other perspectives such as the design of the chatbots and the student experience with the educational chatbots. To sum up, Table 2 shows some gaps that this study aims at bridging to reflect on educational chatbots in the literature. A conversational agent can hold a discussion with students in a variety of ways, ranging from spoken (Wik & Hjalmarsson, 2009) to text-based (Chaudhuri et al., 2009) to nonverbal (Wik & Hjalmarsson, 2009; Ruttkay & Pelachaud, 2006). Similarly, the agent’s visual appearance can be human-like or cartoonish, static or animated, two-dimensional or three-dimensional (Dehn & Van Mulken, 2000).
The fifth question addresses the principles used to design the proposed chatbots. The sixth question focuses on the evaluation methods used to prove the effectiveness of the proposed chatbots. Finally, the seventh question discusses the challenges and limitations of the works behind the proposed chatbots and potential solutions to such challenges.
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