Chatbot for Education: Benefits, Challenges and Opportunities

Moreover, our projects are tailored to each client’s needs, resolving customer pain points. So, partnering with MOCG for your future chatbot development is a one-stop solution to address all concerns from the above. This article sheds light on such tools, exploring their wide-ranging capabilities, limitations, and significant impact on the learning landscape. Read till the end and witness how companies, including Duolingo, leverage innovative technology to make learning accessible to everyone. Regular testing with real users and incorporating their feedback is critical to the success of your chatbot. Each iteration should aim to improve the user experience and streamline communication further.

Interface designers have come to appreciate that humans’ readiness to interpret computer output as genuinely conversational—even when it is actually based on rather simple pattern-matching—can be exploited for useful purposes. Thus, for example, online help systems can usefully employ chatbot techniques to identify the area of help that users require, potentially providing a “friendlier” interface than a more formal search or menu system. This sort of usage holds the prospect of moving chatbot technology from Weizenbaum’s “shelf … reserved for curios” to that marked “genuinely useful computational methods”.

EC studies have primarily focused on language learning, programming, and health courses, implying that EC application and the investigation of learning outcomes have not been investigated in various educational domains and levels of education. Today, chatbots can consistently manage customer interactions 24×7 while continuously improving the quality of the responses and keeping costs down. A chatbot can also eliminate long wait times for phone-based customer support, or even longer wait times for email, chat and web-based support, because they are available immediately to any number of users at once. That’s a great user experience—and satisfied customers are more likely to exhibit brand loyalty. Conversational AI chatbots can remember conversations with users and incorporate this context into their interactions. When combined with automation capabilities including robotic process automation (RPA), users can accomplish complex tasks through the chatbot experience.

Chatbot platforms can help small businesses that are often short of customer support staff. We’ve compared the best chatbot platforms on the web, and narrowed down the selection to the choicest few. Provide a clear path for customer questions to improve the shopping experience you offer. Sharp wave ripples (SPW-Rs) in the brain facilitate memory consolidation by reactivating segments of waking neuronal sequences. AI models like OpenAI’s GPT-4 reveal parallels with evolutionary learning, refining responses through extensive dataset interactions, much like how organisms adapt to resonate better with their environment. Leaders should acknowledge one critical element about AI systems, which is that they are emotionally invasive because they have many apparent similarities with our own ways of behaving, and they communicate through the natural language operating system of our species.

It serves as a valuable resource for students working on advanced projects and in-depth research endeavors. Regarding the frequency of use of the four AICs employed in the intervention, the post-survey results shown in Table 3 indicated that Andy was the most frequently used, averaging nearly 4 h per week, followed by John Bot and Mondly, while Buddy.ai was the least used. Concerning the educational setting, Spanish participants interacted more frequently with all four AICs compared to Czech students. The SD values show a similar level of variation in the weekly interaction hours across all four AICs for both Spanish and Czech participants, suggesting a comparable spread of interaction frequencies within each group.

The study by Pérez et al. (2020) reviewed the existing types of educational chatbots and the learning results expected from them. Smutny and Schreiberova (2020) examined chatbots as a learning aid for Facebook Messenger. Thomas (2020) discussed the benefits of educational chatbots for learners and educators, showing that the chatbots are successful educational tools, and their benefits outweigh the shortcomings and offer a more effective educational experience. Okonkwo and Ade-Ibijola (2021) analyzed the main benefits and challenges of implementing chatbots in an educational setting.

• These examples highlight the lack of readiness to embrace recently developed AI tools.
• Therefore, future studies should look into educators’ challenges, needs, and competencies and align them in fulfill EC facilitated learning goals.
• Conversational agents have been developed over the last decade to serve a variety of pedagogical roles, such as tutors, coaches, and learning companions (Haake & Gulz, 2009).

By creating a sense of connection and personalized interaction, these AI chatbots forge stronger bonds between students and their studies. Learners feel more immersed and invested in their educational journey, driven by the desire to explore new topics and uncover intriguing insights. Moreover, according to Cunningham-Nelson et al. (2019), one of the key benefits of EC is that it can support a large number of users simultaneously, which is undeniably an added advantage as it reduces instructors’ workload. Colace et al. (2018) describe ECs as instrumental when dealing with multiple students, especially testing behavior, keeping track of progress, and assigning tasks.

User Psychographics

While chatbots can handle most queries, there will be times when a human touch is necessary. Ensuring that the handover from bot to human is seamless is a challenge that requires careful design. You can foun additiona information about ai customer service and artificial intelligence and NLP. It is a superfast virtual agent that can accurately reply to customer inquiries. To ensure this, you only need to make sure you train it with your knowledge sources, such as course catalogs and syllabi, policies and procedures. By answering prospective students’ queries on courses, admissions, and the application process, chatbots simplify and speed up the enrolment process.

The User Experience dimension (UEX) revealed that while some AICs were able to provide a moderate level of enjoyment and engagement, overall satisfaction levels were not as positive as expected. This indicates the need for AICs to offer a more personalized learning experience to sustain learner engagement and interest. Like all of us, teachers are bound by time and space — but can educational technology offer new ways to make a teacher’s presence and knowledge available to learners? Stanford d.school’s Leticia Britos Cavagnaro is pioneering efforts to extend interactive resources beyond the classroom. She recently has developed the “d.bot,” which takes a software feature that many of us know through our experiences as customers — the chatbot — and deploys it instead as a tool for teaching and learning.

With a traditional chatbot, the user can use the specific phrase “tell me the weather forecast.” The chatbot says it will rain. With a virtual agent, the user can ask, “What’s tomorrow’s weather lookin’ like? ”—and the virtual agent not only predicts educational chatbots tomorrow’s rain, but also offers to set an earlier alarm to account for rain delays in the morning commute. To increase the power of apps already in use, well-designed chatbots can be integrated into the software an organization is already using.

The solution may be situated in developing code-free chatbots (Luo & Gonda, 2019), especially via MIM (Smutny & Schreiberova, 2020). Conversely, Garcia Brustenga et al. (2018) categorized ECs based on eight tasks in the educational context as described in Table 1. Correspondingly, these tasks reflect that ECs may be potentially beneficial in fulfilling the three learning domains by providing a platform for information retrieval, emotional and motivational support, and skills development.

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. At the same time, the introduction of ECs did not create cognitive barriers that would have affected the cognition, motivational and creative processes involved in project-based learning. Furthermore, as there is a triangulated relationship between these outcomes, the author speculates that these outcomes were justified, especially with the small sample size used, as Rosenstein (2019) explained. 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.

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AI-powered teacher’s assistant

Additionally, chatbots streamline administrative tasks, such as admissions and enrollment processes, automating repetitive tasks and reducing response times for improved efficiency. With the integration of Conversational AI and Generative AI, chatbots enhance https://chat.openai.com/ communication, offer 24/7 support, and cater to the unique needs of each student. A chatbot in the education industry is an AI-powered virtual assistant designed to interact with students, teachers, and other stakeholders in the educational ecosystem.

This constant accessibility allows learners to seek support, access resources, and engage in activities at their convenience. In the context of chatbots for education, effectiveness is commonly measured by the reduction in response times, improvement in student satisfaction scores and the volume of successfully resolved queries. Look for features such as natural language processing, integration capabilities with school databases, scalability, and the ability to handle a wide range of queries. Use structured conversation flows with clear options and avoid jargon that might confuse the user.

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. In general, most desktop-based chatbots were built in or before 2013, probably because desktop-based systems are cumbersome to modern users as they must be downloaded and installed, need frequent updates, and are dependent on operating systems. Unsurprisingly, most chatbots were web-based, probably because the web-based applications are operating system independent, do not require downloading, installing, or updating. According to an App Annie report, users spent 120 billion dollars on application stores Footnote 8. After defining the criteria, our search query was performed in the selected databases to begin the inclusion and exclusion process.

Chatbot for Education: Benefits, Challenges and Opportunities

The vast majority of selected articles were written or co-written by researchers from American universities. However, the research that emerged from all European universities combined was the highest in the number of articles (19 articles). Asian universities have contributed 10 articles, while American universities contributed 9 articles. Finally, universities from Africa and Australia contributed 4 articles (2 articles each). According to the survey conducted among 1,000 secondary school attendees, 67% of learners admitted using AI tools.

The data is captured digitally in a format that can be analyzed manually or by using algorithms that can detect themes, patterns, and connections. In effect the teacher can “interact” with and learn from multiple learners at the same time (in theory an infinite number of them). None of the articles explicitly relied on usability heuristics and guidelines in designing the chatbots, though some authors stressed a few usability principles such as consistency and subjective satisfaction. Further, none of the articles discussed or assessed a distinct personality of the chatbots though research shows that chatbot personality affects users’ subjective satisfaction.

To summarize, the journey through educational chatbots has uncovered a field of possibilities. These AI tools amplify engagement, offer personalized content, and ensure uninterrupted support. Yet, the limitations of these bots, such as lack of emotional intelligence, demand further attention. But the success stories of the University of Galway and Georgia State University, reveal the transformative potential of such models. Additionally, tutoring chatbots provide personalized learning experiences, attracting more applicants to educational institutions. Moreover, they contribute to higher learner retention rates, thereby amplifying the success of establishments.

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 Chat GPT 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).

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). 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.

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Most researchers (25 articles; 69.44%) developed chatbots that operate on the web (Fig. 5). For example, KEMTbot (Ondáš et al., 2019) is a chatbot system that provides information about the department, its staff, and their offices. Other chatbots acted as intelligent tutoring systems, such as Oscar (Latham et al., 2011), used for teaching computer science topics. Moreover, other web-based chatbots such as EnglishBot (Ruan et al., 2021) help students learn a foreign language. The traditional education system faces several issues, including overcrowded classrooms, a lack of personalized attention for students, varying learning paces and styles, and the struggle to keep up with the fast-paced evolution of technology and information.

Moreover, questions to ponder are the ethical implication of using EC, especially out of the learning scheduled time, and if such practices are welcomed, warranted, and accepted by today’s learner as a much-needed learning strategy. Therefore, future studies should look into educators’ challenges, needs, and competencies and align them in fulfill EC facilitated learning goals. Furthermore, there is much to be explored in understanding the complex dynamics of human–computer interaction in realizing such a goal, especially educational goals that are currently being influenced by the onset of the Covid-19 pandemic. Conversely, future studies should look into different learning outcomes, social media use, personality, age, culture, context, and use behavior to understand the use of chatbots for education. Artificial Intelligence (AI) technologies have increasingly become vital in our everyday lives. Education is one of the most visible domains in which these technologies are being used.

Moreover, both classes were also managed through the institution’s learning management system to distribute notes, attendance, and submission of assignments. This is one of the top chatbot platforms for your social media business account. These are rule-based chatbots that you can use to capture contact information, interact with customers, or pause the automation feature to transfer the communication to the agent. Large Language Models (LLMs), such as ChatGPT and BERT, excel in pattern recognition, capturing the intricacies of human language and behavior. They understand contextual information and predict user intent with remarkable precision, thanks to extensive datasets that offer a deep understanding of linguistic patterns. RL facilitates adaptive learning from interactions, enabling AI systems to learn optimal sequences of actions to achieve desired outcomes while LLMs contribute powerful pattern recognition abilities.

This efficiency contributes to higher satisfaction levels among educatee and staff, positively impacting the institution’s credibility. For instance, if trainees were absent, the bot could send notes of lectures or essential reminders, to keep them informed while they’re not present. This efficiency contributes to a more enriching learning experience, consequently attracting more students. The success of chatbot implementation depends on how easily educatee perceive and adapt to their use.

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. Chatbots for education work collaboratively with teachers, optimizing the online learning process and creating an enriched educational ecosystem.

Before you start designing your chatbot, you need to have a clear understanding of your audience. Understanding your users is vital to designing a chatbot that they will engage with. By analyzing conversation data, educational institutions can gain insights into user preferences, pain points, and popular inquiries, informing decision-making and strategy.

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.

What’s more interesting is that 42% of those surveyed apply this technology in math, while 41% use it for writing essays. “With many institutions offering similar programs, such as the numerous universities in Malaysia presenting executive MBAs (Master of Business Administration), acquiring customers becomes a challenge. Chatbots emerge as crucial tools for efficiently managing inquiries and standing out in the competitive field”, he added. 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.

Furthermore, tech solutions like conversational AI, are being deployed over every platform on the internet, be it social media or business websites and applications. Tech-savvy students, parents, and teachers are experiencing the privilege of interacting with the chatbots and in turn, institutions are observing satisfied students and happier staff. Subsequently, motivational beliefs are reflected by perceived self-efficacy and intrinsic values students have towards their cognitive engagement and academic performance (Pintrich & de Groot, 1990). 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).

These programs may struggle to offer innovative or creative solutions to complex problems. This limits their ability to stimulate critical thinking or problem-solving skills. This limitation could impact the overall effectiveness of such tools in promoting creative learning approaches.

Secondly, understanding how different student characteristics interact with chatbot technology can help tailor educational interventions to individual needs, potentially optimizing the learning experience. Thirdly, exploring the specific pedagogical strategies employed by chatbots to enhance learning components can inform the development of more effective educational tools and methods. Educational chatbots (ECs) are chatbots designed for pedagogical purposes and are viewed as an Internet of Things (IoT) interface that could revolutionize teaching and learning.

In the fast-paced educational environment, providing instant assistance is crucial. Chatbots excel at offering immediate support on a 24/7 basis, helping students with queries, and directing them to the appropriate resources. AI aids researchers in developing systems that can collect student feedback by measuring how much students are able to understand the study material and be attentive during a study session. The way AI technology is booming in every sphere of life, the day when quality education will be more easily accessible is not far. For example, Georgia Tech has created an adaptive learning platform for its computer science master’s program. Similarly, Stanford has its own AI Laboratory, where researchers work on cutting-edge AI projects.

Within just eight months of its launch in 2022, it has already amassed over 100 million users, setting new records for user and traffic growth. ChatGPT stands out among AI-powered chatbots used in education due to its advanced natural language processing capabilities and sophisticated language generation, enabling more natural and human-like conversations. It excels at capturing and retaining contextual information throughout interactions, leading to more coherent and contextually relevant conversations. Unlike some educational chatbots that follow predetermined paths or rely on predefined scripts, ChatGPT is capable of engaging in open-ended dialogue and adapting to various user inputs. The latest chatbot models have showcased remarkable capabilities in natural language processing and generation.

Chatbots can help educational institutions in data collection and analysis in various ways. Firstly, they can collect and analyze data to offer rich insights into student behavior and performance to help them create more effective learning programs. Secondly, chatbots can gather data on student interactions, feedback, and performance, which can be used to identify areas for improvement and optimize learning outcomes. Thirdly education chatbots can access examination data and student responses in order to perform automated assessments. The bots can then process this information on the instructor’s request to generate student-specific scorecards and provide learning gap insights. Chatbots today find their applications in more than just customer services and engagement, they have expanded their roles to various fields, including education.

The study highlights a tendency in the literature to use exaggerated language in both dystopian and utopian manner, with some claims about AI chatbots’ potential being unsupported by the empirical evidence. The study examines the emerging research area of Artifical Intelligence (AI) chatbots in Higher Education, focusing specifically on empirical studies conducted since the release of Chat GPT. The review includes 23 research articles published between December 2022 and December 2023 exploring the use of AI chatbots in Higher Education settings. By leveraging the capabilities of chatbots for higher education, institutions can create a thriving learning ecosystem that fosters student success.

Using advanced Conversational AI and Generative AI technologies, chatbots can engage in natural language conversations, providing personalized support and delivering relevant information on various educational topics. Deep learning capabilities enable AI chatbots to become more accurate over time, which in turn enables humans to interact with AI chatbots in a more natural, free-flowing way without being misunderstood. Such chatbots often use deep learning and natural language processing, but simpler chatbots have existed for decades. The third area explores how AICs’ design can positively affect language learning outcomes. Modern AICs usually include an interface with multimedia content, real-time feedback, and social media integration (Haristiani & Rifa’I, 2020). They also employ advanced speech technologies to ensure accessible and humanlike dialogues (Petrović & Jovanović, 2021).

• Jasper Chatbot is a specialist in STEM subjects, simplifying the complexities of mathematics and science.
• Understanding your users is vital to designing a chatbot that they will engage with.
• For example, one chatbot focused on the students’ learning styles and personality features (Redondo-Hernández & Pérez-Marín, 2011).
• Educational institutions may need to rapidly adapt their policies and practices to guide and support students in using educational chatbots safely and constructively manner (Baidoo-Anu & Owusu Ansah, 2023).
• Guided analysis of how AI can affect your own courses and teaching practice, covering ethical issues, student success issues, and workload balance.
• 7, most of the articles (88.88%) used the chatbot-driven interaction style where the chatbot controls the conversation.

A few other subjects were targeted by the educational chatbots, such as engineering (Mendez et al., 2020), religious education (Alobaidi et al., 2013), psychology (Hayashi, 2013), and mathematics (Rodrigo et al., 2012). In comparison, chatbots used to teach languages received less attention from the community (6 articles; 16.66%;). Interestingly, researchers used a variety of interactive media such as voice (Ayedoun et al., 2017; Ruan et al., 2021), video (Griol et al., 2014), and speech recognition (Ayedoun et al., 2017; Ruan et al., 2019).

Navigating the expansive world of educational chatbots reveals a realm where technology meets academia, fostering student engagement, and offering support. These AI-driven programs, tailored for educational settings, aim to provide enriched learning experiences. It’s incredible, but chatbots have been used in education since the early 1970s. When you think of advancements in technology, edtech might not be the first thing that pops into your head. But during the COVID-19 pandemic, edtech became a true lifeline for education by making it accessible and easy to use despite there being numerous physical restrictions.

Today, technologies like conversational AI and natural language processing (NLP) continue to help educators and students world over teach and learn better. Believe it or not, the education sector is now among the top users of chatbots and other smart AI tools like ChatGPT. According to Kumar and Silva (2020), acceptance, facilities, and skills are still are a significant challenge to students and instructors. Similarly, designing and adapting chatbots into existing learning systems is often taxing (Luo & Gonda, 2019) as instructors sometimes have limited competencies and strategic options in fulfilling EC pedagogical needs (Sandoval, 2018). Moreover, the complexity of designing and capturing all scenarios of how a user might engage with a chatbot also creates frustrations in interaction as expectations may not always be met for both parties (Brandtzaeg & Følstad, 2018). Hence, while ECs as conversational agents may have been projected to substitute learning platforms in the future (Følstad & Brandtzaeg, 2017), much is still to be explored from stakeholders’ viewpoint in facilitating such intervention.