Are We There Yet? A Systematic Literature Review on Chatbots in Education
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. 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).
Based on a mixed-method quasi-experimental approach, ECs were found to improve learning performance and teamwork with a practical impact. 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. Nevertheless, Wang et al. (2021) claims while the application of chatbots in education are novel, it is also impacted by scarcity. Nevertheless, while this absence is inevitable, it also provides a potential for exploring innovations in educational technology across disciplines (Wang et al., 2021).
Skills
For example, one chatbot focused on the students’ learning styles and personality features (Redondo-Hernández & Pérez-Marín, 2011). As another example, the SimStudent chatbot is a teachable agent that students can teach (Matsuda et al., 2013). The chatbot used pattern matching to emulate a psychotherapist benefits of chatbots in education conversing with a human patient. It used Artificial Intelligence Markup Language (AIML) to identify an accurate response to user input using knowledge records (AbuShawar and Atwell, 2015). Through turns of conversation, a chatbot can guide, advise, and remedy questions and concerns on any topic.
Understanding student sentiments during and after the sessions is very important for teachers. If students end up being confused and unclear about the topic, all the efforts made by the teachers go in vain. From teachers to syllabus, admissions to hygiene, schools can collect information on all the aspects and become champions in their sector. Henry I. Miller, MS, MD, is the Glenn Swogger Distinguished Fellow at the American Council on Science and Health. His research focuses on public policy toward science, technology, and medicine, encompassing a number of areas, including pharmaceutical development, genetic engineering, models for regulatory reform, precision medicine, and the emergence of new viral diseases.
Chatbots and Artificial Intelligence in Education
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. Expanding on the necessity for improved customization in AICs, the integration of different features can be proposed to enhance chatbot-human personalization (Belda-Medina et al., 2022). These features include the ability to customize avatars (age, gender, voice, etc.) similar to intelligent conversational agents such as Replika.
There are multiple business dimensions in the education industry where chatbots are gaining popularity, such as online tutors, student support, teacher’s assistant, administrative tool, assessing and generating results. The following references provide information about how to communicate standards about artificial intelligence to your students and how you can leverage the benefits of artificial intelligence to facilitate student learning. Understanding why students may inappropriately use AI tools can shed light on the importance of revising your current assignments and assessments.
Qualitative data were collected through class discussions and assessment reports of the AICS following a template provided through the Moodle platform. During the 1-month intervention period in each educational setting, participants independently completed the assessment reports. They were instructed to provide personal feedback on their interaction with each AIC, using the template to note both positive and negative aspects. Additionally, they were asked to attach 12 screenshots illustrating their interaction, three with each AIC, to support their assessment. QDA Miner Software was used for textual analysis of students’ written evaluations on each AIC, adhering to a provided template. Student comments were systematically categorized into potential benefits and limitations following the template structure and then coded using a tree-structured code system, focusing on recurrent themes through frequency analysis.
Consequently, it has prompted a significant surge in research, aiming to explore the impact of chatbots on education. ChatBot distinguishes itself in the customer service sector with its AI customer service chatbot platform, which is independent of third-party AI providers like OpenAI or Google Bard. This platform delivers fast, accurate responses by analyzing your website content, ensuring human-like interactions tailored to your business needs. Chatbots have become a low-cost way to scale your support, accelerate response times, and improve customer experiences. And when designed correctly, chatbots can drive sales, qualify leads, and even onboard new customers. 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).
If you would like more visual formatting and branding control, you can add a third party tool such as BotCopy. With BotCopy, you are able to create a free trial for 500 engagements before you have to choose a plan. This will give you time to test it out and find if this is something you want to pay for. Feedback chatbots also afford a more informal, collegial environment for sharing concerns and successes in a course. This can be helpful when asking for feedback about more delicate topics like points of confusion or a sense of belonging. The more informal environment and gradual, directed questioning via turns of conversation can establish a more personable channel through which to share insights.
- This combination of features positions ChatBot as a leading choice for businesses looking to enhance their customer service experience while maintaining data integrity and operational efficiency.
- Furthermore, with online college applications now being the most popular, the volume of applications has increased significantly, making it more difficult to monitor.
- Feature papers represent the most advanced research with significant potential for high impact in the field.
In some instances, researchers combined multiple evaluation methods, possibly to strengthen the findings. 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. Only four studies (Hwang & Chang, 2021; Wollny et al., 2021; Smutny & Schreiberova, 2020; Winkler & Söllner, 2018) examined the field of application.
Chatbot-human interaction satisfaction model results
Similarly, there was also more emphasis on how they contributed as a team, especially in providing technical support. As for CT, not much difference were observed pre and post-intervention for teamwork; however, the post-intervention in both groups reflected a reduced need for creativity and emphasizing the importance of managing their learning task cognitively and emotionally as a team. Concurrently, it was evident that the self-realization of their value as a contributing team member in both groups increased from pre-intervention to post-intervention, which was higher for the CT group. Firstly, Kearney et al. (2009) explained that in homogenous teams (as investigated in this study), the need for cognition might have a limited amount of influence as both groups are required to be innovative simultaneously in providing project solutions. Lapina (2020) added that problem-based learning and solving complex problems could improve the need for cognition.
- During the 1-month intervention period in each educational setting, participants independently completed the assessment reports.
- Moreover, it was found that ECs facilitated collaboration among team members that indirectly influenced their ability to perform as a team.
- 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.
- This study focuses on the conceptual principles that led to the chatbot’s design.
- GenAI tools won’t always react the way you want them to, but you can usually make tweaks to the prompt you are writing to overcome this.
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 followed approach with these chatbots is asking the students questions to teach students certain content. Moreover, it has been found that teaching agents use various techniques to engage students. User-driven conversations are powered by AI and thus allow for a flexible dialogue as the user chooses the types of questions they ask and thus can deviate from the chatbot’s script.
Authors and Affiliations
Another early example of a chatbot was PARRY, implemented in 1972 by psychiatrist Kenneth Colby at Stanford University (Colby, 1981). It engaged in text-based conversations and demonstrated the ability to exhibit delusional behavior, offering insights into natural language processing and AI. Later in 2001 ActiveBuddy, Inc. developed the chatbot SmarterChild that operated on instant messaging platforms such as AOL Instant Messenger and MSN Messenger (Hoffer et al., 2001). SmarterChild was a chatbot that could carry on conversations with users about a variety of topics.
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. Chatbots deployed through MIM applications are simplistic bots known as messenger bots (Schmulian & Coetzee, 2019). These platforms, such as Facebook, WhatsApp, and Telegram, have largely introduced chatbots to facilitate automatic around-the-clock interaction and communication, primarily focusing on the service industries.
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. Nevertheless, the manual search did not result in any articles that are not already found in the searched databases. According to their relevance to our research questions, we evaluated the found articles using the inclusion and exclusion criteria provided in Table 3. The inclusion and exclusion criteria allowed us to reduce the number of articles unrelated to our research questions. Further, we excluded tutorials, technical reports, posters, and Ph.D. thesis since they are not peer-reviewed.
The advantages and challenges of using chatbots in universities share similarities with those in primary and secondary schools, but there are some additional factors to consider, discussed below. “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).” In an experiment in which the chatbot is asked to design a trendy women’s shoe, it offers several possible alternatives and then, when asked, serially and skillfully refines the design. There are many chatbot providers in the market and picking out the best one that caters to all your education institution necessities is very important. So, choosing a company that offers 24/7 customer support and optimizes processes for faster outputs is very important for long-term commitment and extending trust. With this, constant customer support and maintenance services are offered by the developers of the company.
How will AI chatbots like ChatGPT affect higher education? – University of Rochester
How will AI chatbots like ChatGPT affect higher education?.
Posted: Mon, 27 Feb 2023 08:00:00 GMT [source]
