The Peril and Promise of Chatbots in Education 1 American Council on Science and Health

A thorough, systematic understanding of these determinants, both facilitative and inhibitive, is essential for the more effective deployment and acceptance of such a tool in educational settings, thereby contributing to its ultimate success and efficacy. The current study seeks to shed light on this crucial aspect, aiming to provide a nuanced comprehension of the array of factors that influence university students’ behavioral intentions and patterns towards utilizing ChatGPT for their educational pursuits. While chatbots serve as valuable educational tools, they cannot replace teachers entirely. Instead, they complement educators by automating administrative tasks, providing instant support, and offering personalized learning experiences. Teachers’ expertise and human touch are indispensable for fostering critical thinking, emotional intelligence, and meaningful connections with students.

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). PARRY was a chatbot designed to simulate a paranoid patient with schizophrenia. It engaged in text-based conversations and demonstrated the ability to exhibit delusional behavior, offering insights into natural language processing and AI.

Artificial intelligence (AI) powered chatbots are revolutionizing how we get work done. You’ve likely heard about ChatGPT, but that is only the tip of the iceberg. Millions of people leverage various AI chat tools in their businesses and personal lives.

Moreover, it was found that ECs facilitated collaboration among team members that indirectly influenced their ability to perform as a team. Nevertheless, affective-motivational learning outcomes such as perception of learning, need for cognition, motivation, and creative self-efficacy were not influenced by ECs. Henceforth, this study aims to add to the current body of knowledge on the design and development of EC by introducing a new collective design strategy and its pedagogical and practical implications. The adoption of educational chatbots is on the rise due to their ability to provide a cost-effective method to engage students and provide a personalized learning experience (Benotti et al., 2018). Chatbot adoption is especially crucial in online classes that include many students where individual support from educators to students is challenging (Winkler & Söllner, 2018). Moreover, chatbots may interact with students individually (Hobert & Meyer von Wolff, 2019) or support collaborative learning activities (Chaudhuri et al., 2009; Tegos et al., 2014; Kumar & Rose, 2010; Stahl, 2006; Walker et al., 2011).

Chatbot benefits for businesses and customers

For knowledge application, the self-learning capability of AI tools like ChatGPT can play a significant role. D’mello and Graesser (2013) argued that such AI systems could enhance learners’ ability to apply acquired knowledge, as they can simulate a variety of scenarios and provide immediate feedback. This real-time, contextual problem-solving can further strengthen knowledge application skills in students.

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. The selection of the four AICs, namely Mondly, Andy, John Bot, and Buddy.ai, was guided by specific criteria, including multiplatform compatibility, wide availability, and diverse functionalities such as the integration of different technologies. These AICs offered a wide range of options, such as catering to different English language proficiency levels, providing personalized feedback, adapting to individual learning progress, and incorporating other technologies (AR, VR) in some cases.

Furthermore, according to Tegos et al. (2020), investigation on integration and application of chatbots is still warranted in the real-world educational settings. Therefore, the objective of this study is first to address research gaps based on literature, application, and design and development strategies for EC. Next, by situating the study based on these selected research gaps, the effectiveness of EC is explored for team-based projects in a design course using a quasi-experimental approach. However, the study also highlights the challenges that need to be addressed, such as the requirement for more sophisticated AI algorithms capable of adjusting to the learner’s proficiency level and the improvement of speech technologies. This suggests the need for evolving teaching methods and curricula to more effectively incorporate AICs, emphasizing the enhancement of their capabilities for providing contextually rich and varied linguistic experiences.

How Universities Can Use AI Chatbots to Connect with Students and Drive Success

Our marker variable showed negligible correlations with the study variables, further indicating that CMB is unlikely to have significantly influenced our results. In conclusion, both procedural and statistical checks suggest that CMB is not a major issue in this study, and the relationships identified between the constructs can be interpreted with confidence. Before publishing your first chatbot, there are some tips and tricks that you should be aware of. It allows the business to save on labor costs and allocate resources to other areas like course development or marketing.

• Qualitative data were collected through class discussions and assessment reports of the AICS following a template provided through the Moodle platform.
• Natural Conversational Interaction (#7NCI) pertains to the chatbot’s ability to emulate the natural flow and dynamics of human conversation.
• When using new technologies like AI, it’s best to keep a clear mind about what it is and isn’t.
• It will also help you determine which of the problems are the most pressing and therefore should be first when you’re making your bots.

If you get your bot from a vendor, you’ll pay around $40 per month for the unlimited number of chatbots. It can automate day-to-day tasks that include everything from answering FAQs to booking appointments. This allows your customer support team to concentrate on more complex queries. Bots turn the first-time website visitors into new customers by showing off your new products and offering discounts to tempt potential clients. Automates Repetitive TasksHere is the truth – no one likes doing the same task over and over again. In the case of human beings, repetitive tasks are more prone to mistakes.

Chatbots are available 24/7, offering support to users worldwide at any time. This leads to improved customer satisfaction, as users can access help whenever they need it. Conversational AI is revolutionizing the way businesses communicate with their customers and everyone is loving this new way. Businesses are adopting artificial intelligence and investing more and more in it for automating different business processes like customer support, marketing, sales, customer engagement and overall customer experience. In 2023, AI chatbots are transforming the education industry with their versatile applications.

But one special power of chatbots seems to be that they’re close enough to human to forge a bond with students, yet not human enough to make them uncomfortable. In the cases of CSUN and Georgia State, their chatbots began as an extension of their admissions offices. At CSUN, students were first introduced to CSUNny when they submitted their deposits. The chatbot then guided them through the rest of the enrollment process, benefits of chatbots in education reminding them to stay on top of financial aid applications and helping them stay connected until they visited campus for the first time. But lost in some of the clamor over generative AI tools like ChatGPT is the reality that AI has been a helpful ally to colleges and universities for years. AI tutors have been assisting students since at least 2016, and university-branded chatbots have been around just as long.

However, machines with only limited memory cannot form a complete understanding of the world because their recall of past events is limited and only used in a narrow band of time. First of all, decide whether your bot should use formal or informal language and set the tone that matches your brand. Then, create a wireframe of the chatbot story that includes engaging characteristics. After that, find a unique chatbot icon that will fit your brand and ensure it’s clearly showing that this is a bot.

These automated conversational agents (Riel, 2020) have been significantly used to replicate customer service interaction (Holotescu, 2016) in various domains (Khan et al., 2019; Wang et al., 2021) to an extent it has become a common trend (Wang et al., 2021). The Chatbot-Human Interaction Satisfaction Model (CHISM) is a tool previously designed and used to measure participants’ satisfaction with intelligent conversational agents in language learning (Belda-Medina et al., 2022). This model was specifically adapted for this study to be implemented with AICs.

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. The application of the CHISM model in the evaluation of four AICs has provided valuable insights into the effectiveness of these tools in language learning.

Overall, it can be observed that the group task performed through ECs contributed towards team building and collaboration, whereas for the CT group, the concept of individuality was more apparent. Interestingly, no feedback from the EC group mentioned difficulties in using the EC nor complexity in interacting with it. It was presumed that students welcomed such interaction as it provided learning support and understood its significance. The results show that the chatbots were proposed in various areas, including mainly computer science, language, general education, and a few other fields such as engineering and mathematics. Most chatbots are accessible via a web platform, and a fewer chatbots were available on mobile and desktop platforms.

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. As such, we classify the interactions as either chatbot or user-driven. Only four studies (Hwang & Chang, 2021; Wollny et al., 2021; Smutny & Schreiberova, 2020; Winkler & Söllner, 2018) examined the field of application. None of the studies discussed the platforms on which the chatbots run, while only one study (Wollny et al., 2021) analyzed the educational roles the chatbots are playing. The study used “teaching,” “assisting,” and “mentoring” as categories for educational roles.

What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education

This indicates the need for AICs to offer a more personalized learning experience to sustain learner engagement and interest. The proliferation of smartphones in the late 2000s led to the integration of educational chatbots into mobile applications. However, the initial models were basic, relying on a scripted question–answer format and not intended for meaningful practice beyond their specific subject area (Godwin-Jones, 2022). Since then, AI technology has significantly advanced and chatbots are now able to provide more comprehensive Chat GPT language learning support, such as conversational exchange, interactive activities, and multimedia content (Jung, 2019; Li et al., 2022). In terms of application, chatbots are primarily used in education to teach various subjects, including but not limited to mathematics, computer science, foreign languages, and engineering. While many chatbots follow predetermined conversational paths, some employ personalized learning approaches tailored to individual student needs, incorporating experiential and collaborative learning principles.

Personalization also extends to recognizing users’ specific learning goals and offering resources or guidance to help them achieve these goals. This feature can increase the value that users derive from their interactions with the chatbot (Jo, 2022). Lastly, the novelty value of AI chatbots in educational contexts cannot be underestimated. Developers can capitalize on this by introducing innovative features and interaction modes that keep users engaged and make learning exciting. Such features could include gamified learning activities, integration with other learning resources, and more interactive and responsive dialogue systems.

All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the Creative Commons licensing terms apply. The authors declare that this research paper did not receive any funding from external organizations. The study was conducted independently and without financial support from any source. The authors have no financial interests or affiliations that could have influenced the design, execution, analysis, or reporting of the research. Two popular platforms, Shopify and Etsy, have the potential to turn those dreams into reality.

Students that struggle with specific materials can be provided individualized learning materials based on the information collected. In addition, this collected data can provide educators and administration with useful information to profile and predict the likelihood of success a student may have in a course (Zawacki-Richter et al., 2019). This means that teachers can develop systems to identify students at risk of failing and offer appropriate guidance and intervention.

It pays off to customize your messages to clients and provide more personalized customer service. What’s more, is that chatbots can collect customer feedback that is aimed at improving your products and services according to the customer’s needs. You can foun additiona information about ai customer service and artificial intelligence and NLP. You can do this by going through the chats and looking for common themes. Another chatbot advantage is that it can collect customer data, such as name, email address, and other information. You can also embed a customer satisfaction survey at the end of the bot’s conversation to see how happy your customers are with your brand.

We bet you would have heard music playing when you call a customer care agent expecting a quicker response. Since they are automated to answer what customers ask, they answer instantly without getting tired. It also makes things much simpler when approaching international markets because you no longer have to be concerned about hiring fluent-speaking customer executives from other countries.

Chatbots can also be programmed to have a personality, sound natural and humorous, making them appear more humanlike. The findings of this study have numerous practical implications, particularly for educators, students, and developers of AI chatbot technologies. By integrating the TPB into the analysis, this study not only aligns with established psychological theories of behavior but also expands the application of these theories in the context of AI technology adoption in education. The affect theory, proposed by Lazarus (1991), offers a comprehensive framework for understanding how emotions influence behavior, including technology use. According to this theory, emotional responses are elicited by the cognitive appraisal of a given situation and these emotional responses, in turn, have a significant impact on individuals’ behavior.

Perception of learning

If you are a Microsoft Edge user seeking more comprehensive search results, opting for Bing AI or Microsoft Copilot as your search engine would be advantageous. Particularly, individuals who prefer and solely rely on Bing Search (as opposed to Google) will find these enhancements to the Bing experience highly valuable. Claude has a simple text interface that makes talking to it feel natural.

As users find these tailored experiences new and intriguing, the novelty value of the AI tool is enhanced. Similarly, as the AI tool continuously learns and adapts to individual users’ needs, it helps learners achieve their learning objectives more effectively and efficiently. This aligns with the perception of increased benefits, as users recognize the tool’s contribution to their learning outcomes. However, the degree of this impact varies, suggesting that personalization’s impact may be contingent on other factors that need exploration.

As mentioned previously, the goal can be purely administrative (Chocarro et al., 2021) or pedagogical (Sandoval, 2018). According to Schmulian and Coetzee (2019), there is still scarcity in mobile-based chatbot application in the educational domain, and while ECs in MIM has been gaining momentum, it has not instigated studies to address its implementation. Furthermore, there are https://chat.openai.com/ also limited studies in strategies that can be used to improvise ECs role as an engaging pedagogical communication agent (Chaves & Gerosa, 2021). Besides, it was stipulated that students’ expectations and the current reality of simplistic bots may not be aligned as Miller (2016) claims that ANI’s limitation has delimited chatbots towards a simplistic menu prompt interaction.

As AI continues to advance, chatbots are becoming more diverse in their capabilities and functionalities. Hence, research considering different kinds of AI chatbots, their specific features, and their effects on learning outcomes could provide further insight. Moreover, this study primarily focuses on individual impacts and user perceptions. Future research could also look into institutional perspectives and the macro-level impacts of AI chatbot adoption in education. For instance, studies could investigate how the implementation of AI chatbots affects teaching strategies, curriculum development, and institutional resource allocation.

This cost-effective approach ensures that educational resources are utilized efficiently, ultimately contributing to more accessible and affordable education for all. These educational chatbots are like magical helpers transforming the way schools interact with students. Now we can easily explore all kinds of activities related to our studies, thanks to these friendly AI companions by our side. 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.

ChatGPT

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. In general, the studies conducting evaluation studies involved asking participants to take a test after being involved in an activity with the chatbot. The results of the evaluation studies (Table 12) point to various findings such as increased motivation, learning, task completeness, and high subjective satisfaction and engagement. One of them presented in (D’mello & Graesser, 2013) asks the students a question, then waits for the student to write an answer. Then the motivational agent reacts to the answer with varying emotions, including empathy and approval, to motivate students.

Consequently, recognizing the potential of self-learning in AI, such as in ChatGPT, to improve both knowledge acquisition and application among university students, this study suggests the following hypotheses. This study focuses on using chatbots as a learning assistant from an educational perspective by comparing the educational implications with a traditional classroom. Therefore, the outcomes of this study reflected only on the pedagogical outcomes intended for design education and project-based learning and not the interaction behaviors. As users, the students may have different or higher expectations of EC, which are potentially a spillover from use behavior from chatbots from different service industries.

The basic premise of this test is that if a single factor emerges from a factor analysis of all the items in the questionnaire or one general factor accounts for the majority of the covariance among the measures, then common method variance is a problem. In our case, the first of included factors explained 33.88% of the total variance, suggesting that CMB is not a substantial concern in this study. Additionally, we used the marker variable technique, which involves selecting a variable that is theoretically unrelated to other variables in the study and checking for its correlation with them. If significant correlations exist, they might be due to common method variance.

Another pivotal factor is the scope for knowledge acquisition and application. As ChatGPT assists in knowledge acquisition, students can simultaneously apply their newly garnered knowledge, potentially bolstering their academic performance. Personalization of the AI forms another significant enabling factor, ensuring that the interactions are fine-tuned to each student’s distinctive needs, thereby promoting a more individual-centered learning experience. Lastly, the novelty value that ChatGPT brings to the educational realm can stimulate student engagement, making learning an exciting and enjoyable process. Unraveling the influence of these factors on students’ behavior toward ChatGPT could yield valuable insights, paving the way for the tool’s effective implementation in education. This study report theoretical and practical contributions in the area of educational chatbots.

Before you start enjoying any of the benefits, you need to spend some time setting the chatbots up. They can be tricky to install and set up, especially if the bot is complex. In total, you will probably need about 2 weeks to set up and get to know all the functionalities of your chatbot. You can meet customer expectations from many regions of the world by helping them out in their native language. This helps the client to explain their issues clearer and get useful support.

Educational institutions can also provide guidance on safe and responsible use of AI technologies to further assuage these concerns. Technophobia, or the fear of technology, can also hinder the acceptance and use of AI chatbots. To address this, developers can design user-friendly interfaces, provide extensive user support, and gradually introduce advanced features to avoid overwhelming users. Moreover, educational institutions can play a vital role in mitigating technophobia by offering appropriate training and support to help students become comfortable with using these technologies.

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. However, there have been contradictory findings related to critical thinking, learning engagement, and motivation. Deng and Yu (2023) found that chatbots had a significant and positive influence on numerous learning-related aspects but they do not significantly improve motivation among students.

Thirdly, exploring the specific pedagogical strategies employed by chatbots to enhance learning components can inform the development of more effective educational tools and methods. AI-powered chatbots are designed to mimic human conversation using text or voice interaction, providing information in a conversational manner. Chatbots’ history dates back to the 1960s and over the decades chatbots have evolved significantly, driven by advancements in technology and the growing demand for automated communication systems. Created by Joseph Weizenbaum at MIT in 1966, ELIZA was one of the earliest chatbot programs (Weizenbaum, 1966).

• The model, which comprises three dimensions (LEX, DEX, UEX), has allowed for a comprehensive assessment of the AICs across multiple facets.
• Creswell (2012) explained that education-based research in most cases requires intact groups, and thus creating artificial groups may disrupt classroom learning.
• According to Garcia Brustenga et al. (2018), EC can be designed without educational intentionality where it is used purely for administrative purposes to guide and support learning.
• Funding for technology support should be taken into consideration by the administration when deciding whether to adopt chatbot technologies in education.
• Chatbot technology has evolved rapidly over the last 60 years, partly thanks to modern advances in Natural Language Processing (NLP) and Machine Learning (ML) and the availability of Large Language Models (LLMs).
• Users should provide feedback to OpenAI, Google, and other relevant creators and stakeholders regarding any concerns or issues they encounter while using chatbots.

Furthermore, the research goes beyond by illustrating that user-perceived benefits are influenced by both AI chatbot personalization and individual impact, the latter being a synergistic result of knowledge acquisition and application. This interplay provides a nuanced view of the factors contributing to perceived benefits, deepening our theoretical grasp of what renders AI chatbot usage beneficial. By presenting a complex, interconnected model of AI chatbot usage, this research contributes to a more thorough understanding of the dynamics involved. It encourages a multi-dimensional approach in examining AI chatbots’ adoption and usage factors.

For instance, Mondly’s reliance on pre-programmed responses and Buddy.ai’s focus on repetitive drills for children limit dynamic conversation, resulting in lower satisfaction in maintaining contextually relevant dialogues. Although Andy scores slightly higher, it still reveals a need for more adaptable conversation styles for advanced learners. The satisfaction levels in the LEX dimension may also depend on the chatbots’ design relative to students’ levels, with significant differences observed among the four AICs. For example, while Buddy.ai is oriented towards developing oral skills in children at a lower level, John Bot and Andy are designed for vocabulary and grammar building through role-playing interactions at more intermediate levels.

It has been suggested that familiarity and understanding significantly reduce technophobia (Khasawneh, 2018a; Xi et al., 2022). Guilt feeling, as revealed in this study, is another factor that negatively influences behavioral intention. This can occur when users feel guilty about relying heavily on AI chatbots for learning or assignments. To address this, educational institutions should foster an environment that encourages balanced use of AI tools. This could involve providing guidelines on ethical AI use, setting boundaries on AI tool usage in assignments, and integrating AI tools as supplemental rather than primary learning resources.

Several studies have found that educational chatbots improve students’ learning experience. For instance, Okonkwo and Ade-Ibijola (2021) found out that chatbots motivate students, keep them engaged, and grant them immediate assistance, particularly online. Additionally, Wollny et al. (2021) argued that educational chatbots make education more available and easily accessible.