Rexy, A Configurable Application for Building Virtual Teaching Assistants

Benedetto, Luca; Cremonesi, Paolo

doi:10.1007/978-3-030-29384-0_15

Cited by 8 publications

(10 citation statements)

References 13 publications

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“…Table 4. shows that ten (27.77%) articles presented general-purpose educational chatbots that were used in various educational contexts such as online courses (Song et al, 2017;Benedetto & Cremonesi, 2019;Tegos et al, 2020). The approach authors use often relies on a general knowledge base not tied to a specific field.…”

Section: Resultsmentioning

confidence: 99%

“…The mobile-based chatbots were used for various purposes. Examples include Rexy (Benedetto & Cremonesi, 2019), which helps students enroll in Fig. 4 The fields of the chatbots in the selected articles (Griol et al, 2011;Latham et al, 2011;Tegos et al, 2015;Benotti et al, 2017;Clarizia et al, 2018;Coronado et al, 2018;Verleger & Pembridge, 2018;Ondáš et al, 2019;Lee et al, 2020;Qin et al, 2020;Winkler et al, 2020;Daud et al, 2020) General 10 (Redondo-Hernández & Pérez-Marín, 2011;Song et al, 2017;Schouten et al, 2017;Benedetto & Cremonesi, 2019;Janati et al, 2020;Tegos et al, 2020;Villegas-Ch et al, 2020;Zedadra et al, 2014;da Silva Oliveira et al, 2019) ...…”

Section: Rq2: What Platforms Do the Proposed Chatbots Operate On?mentioning

confidence: 99%

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Interacting with educational chatbots: A systematic review

et al. 2022

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Chatbots hold the promise of revolutionizing education by engaging learners, personalizing learning activities, supporting educators, and developing deep insight into learners’ behavior. However, there is a lack of studies that analyze the recent evidence-based chatbot-learner interaction design techniques applied in education. This study presents a systematic review of 36 papers to understand, compare, and reflect on recent attempts to utilize chatbots in education using seven dimensions: educational field, platform, design principles, the role of chatbots, interaction styles, evidence, and limitations. The results show that the chatbots were mainly designed on a web platform to teach computer science, language, general education, and a few other fields such as engineering and mathematics. Further, more than half of the chatbots were used as teaching agents, while more than a third were peer agents. Most of the chatbots used a predetermined conversational path, and more than a quarter utilized a personalized learning approach that catered to students’ learning needs, while other chatbots used experiential and collaborative learning besides other design principles. Moreover, more than a third of the chatbots were evaluated with experiments, and the results primarily point to improved learning and subjective satisfaction. Challenges and limitations include inadequate or insufficient dataset training and a lack of reliance on usability heuristics. Future studies should explore the effect of chatbot personality and localization on subjective satisfaction and learning effectiveness.

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Section: Resultsmentioning

confidence: 99%

Section: Rq2: What Platforms Do the Proposed Chatbots Operate On?mentioning

confidence: 99%

Interacting with educational chatbots: A systematic review

et al. 2022

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“…Similar work has been done by other researchers who built a platform, Rexy for creating virtual TAs for various courses also built on top of IBM Watson [5]. The authors described Rexy, an application they built which can be used to build virtual teaching assistants for different courses and also presented a preliminary evaluation of one such virtual teaching assistant in a user study.…”

Section: Virtual Teaching Assistantsmentioning

confidence: 78%

“…Additionally, the technical details of some of the systems and their evaluations both offline and online are not available (e.g. ( [3,4,5] and hence they are not that easy to build upon and compare with. Our work provided these details along with our systematic evaluation to allow other researchers to replicate our work and also build upon it to create similar systems for their context.…”

Section: Virtual Teaching Assistantsmentioning

confidence: 99%

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Kwame: A Bilingual AI Teaching Assistant for Online SuaCode Courses

Boateng

2021

Lecture Notes in Computer Science

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Introductory hands-on courses such as our smartphone-based coding courses, SuaCode require a lot of support for students to accomplish learning goals. Online environments make it even more difficult to get assistance especially more recently because of COVID-19. Given the multilingual context of our students -learners across 38 African countries -in this work, we developed an AI Teaching Assistant (TA) -Kwame -that provides answers to students' coding questions from our SuaCode courses in English and French. Kwame is a Sentence-BERT(SBERT)-based question-answering (QA) system that we trained and evaluated using question-answer pairs created from our course's quizzes and students' questions in past cohorts. It finds the paragraph most semantically similar to the question via cosine similarity. We compared the system with TF-IDF and Universal Sentence Encoder. Our results showed that SBERT performed the worst for the duration of 6 secs per question but the best for accuracy and fine-tuning on our course data improved the result.

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