The DOI matches its Crossref record; 53 of the 54 references it cites were found in a registry or an index.
Using chatbots to support student goal setting and social presence in fully online activities: learner engagement and perceptions
Open accessAbstract
Although fully online learning is now the 'new normal' in many parts of the world, its implementation is often beset by challenges such as the lack of student self-regulation, and the sense of isolation. In this paper, we explored the use of chatbots to support student goal setting (Study 1) and social presence (Study 2) in online activities. In Study 1, participants in a fully online course were invited to complete a goal setting activity prior to attending class via a goal-setting chatbot. The chatbot engaged participants with five questions developed based on the SMART (specific, measurable, achievable, realistic, and timely) goal setting framework. In Study 2, English-as-Foreign-Language participants in a fully online course were tasked to complete listening practices. The learning buddy chatbot was designed based on the social presence framework (interpersonal communication, open communication, cohesive communication) to guide students through listening exercises. In both Study 1 and 2, we evaluated participants' behavioral engagement by measuring their conversation records with the chatbots, as well as participants' perceived usefulness and ease of use of the chatbots. We also gathered in-depth interview data concerning the participants' perceptions of interacting with the chatbots. Overall, our findings found positive learner experiences with both chatbots with regard to the chatbots' perceived usefulness and perceived ease of use. We also provided suggestions for instructors to apply chatbots in teaching and learning.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · cited 3×
“Perceived usefulness is “the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989, p. 320).”
BackgroundMethodInfluentialJournal of Physics: Conference Series
Jui, J. J., Mohd Helmi, S., Mohd Riduwan, G., Mohd Ashraf, A., & Mohd Zaidi, M. T. (2013) · Journal of Physics Conference Series · doi:10.1088/issn.1742-6596 · Cited by 15,799
Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-013-0528-y · Cited by 12,038 · cited 1×
“Purposeful sampling is widely used in qualitative research to identify information-rich cases related to the phenomenon of interest (Palinkas et al., 2015).”
BackgroundCritical Inquiry in a Text-Based Environment: Computer Conferencing in Higher Education
Garrison, D. R., Anderson, T., & Archer, W. (1999) · The Internet and Higher Education · doi:10.1016/s1096-7516(00)00016-6 · Cited by 6,619 · cited 1×
“Social presence can be defined as “the ability of participants to project themselves socially and emotionally, as real people” (Garrison et al., 1999, p. 94).”
BackgroundELIZA—a computer program for the study of natural language communication between man and machine
Weizenbaum, J. (1966) · Communications of the ACM · doi:10.1145/365153.365168 · Cited by 4,452 · cited 1×
“The first chatbot, ELIZA, developed in 1966, simulated a psychotherapist’s conversation with humans, where users could communicate with ELIZA by entering text inputs, and the chatbot would respond in kind (Weizenbaum, 1966).”
Machines and Mindlessness: Social Responses to Computers
Nass, C., & Moon, Y. (2000) · Journal of Social Issues · doi:10.1111/0022-4537.00153 · Cited by 4,022 · cited 2×
“According to social response theory (Nass & Moon, 2000), human–computer interactions are basically social where humans have the innate tendency to perceive computers as social beings, even when they know that machines do not have feelings or intentions.”
BackgroundDevelopment and use of the ARCS model of instructional design
Keller, J. M. (1987) · Journal of Instructional Development · doi:10.1007/bf02905780 · Cited by 2,332 · cited 2×
“It is therefore suggested that in the long-term use of chatbot, it is important to add variety to sustain the interest in learning with the chatbot (Keller, 1987), by either simplifying or making some changes to the reoccurring sentences.”
BackgroundSelf-regulation through goal setting
Latham, G. P., & Locke, E. A. (1991) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(91)90021-k · Cited by 1,373
AI-based chatbots in customer service and their effects on user compliance
Adam, M., Wessel, M., & Benlian, A. (2021) · Electronic Markets · doi:10.1007/s12525-020-00414-7 · Cited by 1,195 · cited 3×
“By interacting with an anthropomorphized machine, a user may perceive a sense of social presence (Adam et al., 2021).”
BackgroundInfluentialFrom cognitive modeling to self-regulation: a social cognitive career path.
Zimmerman, B. J. (2013) · Educational Psychologist · doi:10.1080/00461520.2013.794676 · Cited by 991 · cited 1×
“These self-reflections can influence subsequent forethought processes (Zimmerman, 2013).”
Background
Research works printed but not linked: 1
- Doran, G. T. (1981). There’s a SMART way to write management’s goals and objectives. Management Review, 70(11), 35–36.
Cited by
Sorted by citations
Items 201–244 of 244
Liu, Z., Xing, W., Jiang, Y., Li, C., Kim, T. H., & Li, H. (2025) · Journal of Computing in Higher Education · doi:10.1007/s12528-025-09468-y · Cited by 3
“Over the past few decades, online learning has become an essential component of higher education (Hew et al., 2023 ), presenting students flexible and accessible learn - ing opportunities and resources (Ayu, 2020 ; Kara, 2020 ).”
Lee, S.-S., Kim, J., Yu, S., & Li, N. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-05891-9 · Cited by 3
“Studies found that students in online learning environments often experience greater autonomy but may struggle with feelings of isolation compared to their of fl ine counterparts (Hew et al., 2023; Li et al., 2022).”
Dwikoranto, D., Lintangesukmanjaya, R. T., Astutik, W., Mubarok, W. K., Putra, D. A., Setiani, R., & Bergsma, L. N. (2025) · Journal of Physics Conference Series · doi:10.1088/1742-6596/3132/1/012013
Chatbots-based Systematic Review for Student Learning and Engagement with LLMs
Rohini, S., & Sethukarasi, T. (2025) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici65217.2025.11252629
“Hew et al. [15] It is capable of tracking student engagement through conversation records.”
InfluentialMuthmainnah, M., Ingilan, S. S., Cardoso, L., Vargheese, K. J., Apriani, E., Yakin, A. M. A., & Obaid, A. J. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-3967-2.ch009
Asrifan, A., Vivekanantharasa, R., Aini, N., Azar, A. S., & Jakob, J. C. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0275-1.ch002
Leppla, L. C., & Seibert, J. (2025) · Chemistry Teacher International · doi:10.1515/cti-2025-0021
“60,61 AI-based learning environments and tutors have proven e ff ective in fostering SRL by adapting task di ffi culty and providing real-time, personalized feedback that strengthens planning and re fl ection skills.”
Isaias, P. T., Hoque, T. T., & Miranda, P. (2025) · Cognition and exploratory learning in the digital age · doi:10.1007/978-3-032-05817-1_6
Marzuki, Cahyono, B. Y., Ivone, F. M., & Wulyani, A. N. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.102438
Critical Thinking and ChatGPT: Navigating Diversity in English Language Education
Liang, W., & Cheng, X. (2026) · Springer eBooks · doi:10.1007/978-3-032-11430-3_8
GPTalk: LLM-based virtual companions for metacognitive growth in self-regulated e-learning
Jung, I.-T., Lee, C., Baek, I.-C., Oh, D., Choi, Y., Kim, K., Kong, D.-J., & Hong, J.-H. (2026) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2026.103754
Hsieh, C., Bali, S., Chiu, T. K. F., Yen, A.-C., Liu, M.-C., Chen, T.-C., & Ardhyantama, V. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106834 · Cited by 1
Assessing Student Perceptions of LLM-Based Conversational Agents for Foreign Language Learning
Pitarch, R. C. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6420-9.ch002
Huang, Y., Liu, F., Liu, D., Liu, W., & Bu, R. (2026) · Systems · doi:10.3390/systems14050492
Kellam, H., Cortés, L. E. P., Dange, A., & Sterling, K. (2026) · doi:10.2196/preprints.100513
The Construction of an AI-based chatbot to Provide Formative Feedback on Argumentative Writing
Wen, J., & Wang, X. (2026) · International Conference on Educational and Information Technology · doi:10.1109/iceit68991.2026.11521774
“During conversations, they can give students instant feedback, and they are available for continuous practice at any time and location [5, 6].”
Using chatbot in education – A systematic review and research agenda
Wut, T. M., Low, M. P., & Tam, K.-W. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.102956
Wardhana, C. K., & Susilana, R. (2026) · IJORER International Journal of Recent Educational Research · doi:10.46245/ijorer.v7i3.1424
Digital and AI Competencies in Foreign Language Training: Approaches for Translators vs. Engineers
Zheleznyakova, O., Kopylova, N. A., & Анатольевна, С. М. (2026) · International Conference on Information Technologies in Engineering Education (Inforino) · doi:10.1109/inforino68543.2026.11556885
Beyond tools to teammates: A multilevel study of explainable ChatGPT in virtual team learning
Darban, M. (2026) · The Internet and Higher Education · doi:10.1016/j.iheduc.2026.101102 · Cited by 1
Zhang, J., Xu, B., Liu, Y., Wenqi, C. A. I., Wu, L., Qi, S., Peng, Y., Yu, J., Long, T., & Ge, B. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.107268
Li, M., Gillies, R. M., & Carroll, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7006278
ERGENE, B. Ç., Ergene, Ö., & Kontorovich, I. (2026) · International Journal of Mathematical Education in Science and Technology · doi:10.1080/0020739x.2026.2687118 · Cited by 2
Ibrahim, A., & Nemt-allah, M. A. (2026) · Scientific Reports · doi:10.1038/s41598-026-62798-4 · Cited by 3
Udo, M.-A. A., Imafidon, F. I., & Godwin³, A. (2026) · Research Square · doi:10.21203/rs.3.rs-10443013/v1
Enhancing learning from video lectures with proactive AI chatbots
Pi, Z., Zhou, D., Wei, Y., Zheng, S., & Li, X. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-14081-2 · Cited by 1
“Research has shown that AI chatbots can enhance learning outcomes, including explicit reasoning, academic performance, and social presence in online learning environments (Hew et al., 2023 ; Liang et al., 2024 ; Okonkwo & Ade-Ibijola, 2021 ).”
Fang, F., Meng, Y., Tang, L., & Cui, Y. H. (2026) · BMC Psychology · doi:10.1186/s40359-026-05198-7
“Hew et al. [21], however, showed that chatbots can support goal setting when interaction is deliberately structured around pedagogical activity.”
Social Presence and AI-Mediated Communication
Sadek, M. (2026) · Springer eBooks · doi:10.1007/978-3-031-52643-5_282-1
Fu, W. (2026) · Research Square · doi:10.21203/rs.3.rs-10392581/v1
TEACHER-CURATED AI FOR COMMUNICATIVE CHINESE ACQUISITION IN UNIVERSITIES OF KAZAKHSTAN
Kereibayeva, A. (2026) · Scientific Journal of Astana IT University · doi:10.37943/lhfx3759
Ayinde, L. A., Katta, K., & Adebayo, J. O. (2026) · Journal of Science and Technology Policy Management · doi:10.1108/jstpm-01-2025-0010
Visualizing the Knowledge Mapping of Chatbots in Education: A Bibliometric Analysis in CiteSpace
Ling, K.-H., & Zhang, S. (2026) · International Conference on Education and Training Technologies (ICETT) · doi:10.1109/icett70124.2026.11666085
“In the teaching process, Hew et al. [37] found that chatbots designed based on the SMART goal framework can bring a positive experience to learners, characterized by high perceived usefulness and ease of use.”
Rizki, L. M., Kusumah, Y. S., Jupri, A., Priatna, B. A., Aridanthy, V., ., S., & Nurlita, M. (2026) · F1000Research · doi:10.12688/f1000research.180626.1
Hyper-Personalized Virtual Companion: Generative Tamagotchi to Cultivate Personal Goals
Sun, Q., & Hsiao, I. (2026) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/3800935.3830854
Pang, J., & Couto, U. S. (2026) · Journal of Convention & Event Tourism · doi:10.1080/15470148.2026.2733161
Wang, Y., Zhang, H., & Cui, Y. (2026) · SAGE Open · doi:10.1177/21582440261491028
Fang, L., Li, Z., Xiong, B., & Lu, Z. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1832451
“Through dynamic interaction and adaptive support, GenAI may make learning more engaging and less burdensome, further increasing intrinsic motivation (Hew et al., 2023).”
Nguyen, M. T., Thach, K. T. D., Nguyen, C. N. L., Nguyen, A. C., & Doan, H. K. (2026) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/8063779
Wen, H. (2026) · Journal of Education Humanities and Social Sciences · doi:10.54097/r9pd4524
Rizki, L. M., Kusumah, Y. S., Jupri, A., Priatna, B. A., Aridanthy, V., ., S., & Nurlita, M. (2026) · F1000Research · doi:10.12688/f1000research.180626.2
From RAG Metrics to Quality of Experience and Trust: An Extended TAM Study of a University Chatbot
Duranec, N., & Dumić, E. (2026) · IEEE Access · doi:10.1109/access.2026.3741793
“Chatbots support goal setting, social presence, help-seeking, self-evaluation, and listening practice, with evaluations based on behavioral engagement, perceived usefulness, perceived ease of use, and qualitative feedback [23], [24].”
Biswas, M. I., Talukder, M. S., & Opel, M. I. T. (2026) · Research Square · doi:10.21203/rs.3.rs-10395816/v1
Designing for AI Ethics Pedagogy Through Multiliteracies Framework
Avcı, D., Durgut, G., & Leila, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0373-2.ch006
Metadata
- Authors
- Hew, K. F., Huang, W., Du, J., & Jia, C.
- Affiliations
- University of Hong Kong (HK)
- Zhejiang University (CN)
- Corresponding author
- Hew, Khe Foon
- Venue
- Journal of Computing in Higher Education
- Published
- 2022-09-09
- Volume
- 35
- Issue
- 1
- Pages
- 40-68
- ISSN
- 1042-1726 (ISSN-L)
- 1867-1233
- PMC
- PMC9458299
- OpenAlex
- W4295753326
- Cited by
- 245
- References
- 54
- Citations by year
- 2026: 53 · 2025: 89 · 2024: 59 · 2023: 43 · 2022: 1
- FWCI
- 15.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Computing in Higher Education (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- University of Hong Kong (Teaching Development Grant)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Online and Blended Learning
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- chatbots, student goal setting, social presence, fully online learning, student engagement, student self-regulation, learner isolation, SMART goals, English as a foreign language, listening practices, perceived usefulness, perceived ease of use, learner perceptions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review