由於國際社會長期以來對原住民傳統醫療知識的分類不足與存在迷失概念，因此導致傳統醫療知識體系面臨極度臨瀕，加上缺乏具備跨領域學習的應用工具，學習者在進行學習的過程中普遍都會遇到困難。聊天機器人具有鮮明的跨領域特徵，也是一種人機互動的對話方式，然而，相關的研究仍然分散在各學科的領域當中。本研究旨在開發雙層次測驗聊天機器人，透過繪製心智圖的知識建構而進行跨領域學習。為了瞭解「導入心智圖決策引導模式的雙層次測驗聊天機器人」學習模式對學習者的影響與學習感受。本研究透過準實驗設計於一所國中的山林教育課程中進行學習活動。受測對象總共有四個班級的八年級學習者，其中的兩個班為實驗組，共計有52人，採用基於心智圖的雙層次測驗聊天機器人進行學習; 另外的兩班則為控制組，共計有56人，採用一般雙層次測驗聊天機器人進行學習。研究結果顯示，實驗組的學習者在學習成就、延宕後測、內在動機、課堂參與、個人自我效能、內在認知負荷與增生認知負荷等方面皆比控制組的學習者具有顯著效果。換句話說，繪製心智圖雖然能夠提升實驗組學習者的內在認知負荷，相對也提升長期記憶，然而，這種情況卻不會增加外在認知負荷。另外，根據質性訪談的結果發現，相較於兩組而言，實驗組的學習感受數量大於控制組，實驗組的回饋內容大多強調雙層次測驗聊天機器人應用於繪製心智圖的跨領域學習功能與專屬網站特色，控制組則是著重在人機互動的便利性、自主性與自律學習。

Owing to a long history of inadequate categorisation and misconceptions of traditional Aboriginal medical knowledge in the international community, which has led to an extremely dangerous endangerment of the traditional medical knowledge system, and the lack of application tools with interdisciplinary learning, learners generally encounter difficulties in the process of learning. Chatbots have interdisciplinary learning characteristics and forms of human-computer interactive dialogue, yet research is still scattered across disciplines and applications. This study aims to develop a chatbot with “mind mapping-based two-tier test chatbot” for interdisciplinary learning through knowledge construction by mind-mapping. In order to understand the impact of the learning model of the “mind mapping-based two-tier test chatbot, MTT chatbot” on learners. A quasi-experimental design was used to conduct learning activities in a junior high school's “mountain forest education” curriculum. A total of four classes of eighth-grade learners were tested. Two classes were the experimental group with 52 learners, and the other two classes were the control group with 56 learners, using the “conventional two-titer test-based chatbot”. The results showed the experimental group had significant effects on learning achievement, delayed post-test, intrinsic motive, classroom engagement, individual self-efficacy, intrinsic cognitive load, and germane cognitive load. The results demonstrate although mind mapping can increase the intrinsic cognitive load of the experimental group and long-term memory, it does not lead to an increase in the extrinsic cognitive load. In addition, according to the results of the qualitative interviews, compared to the two groups, the number of learning feelings given by the experimental group was greater than that of the control group. the feedback from the experimental group mostly emphasis on interdisciplinary learning functions and dedicated website features of the “MTT chatbot”, while the control group focused on the convenience of human-computer interaction, autonomy and self-regulated learning.

Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 427-445.
Adamopoulou, E., & Moussiades, L. (2020). Chatbots: History, technology, and applications. Machine Learning with Applications, 2, 100006.
Adodo, S. O.(2013). Effects of Two-Tier Multiple Choice Diagnostic Assessment Items on Student’s Learning Outcome in Basic Science Technology (BST). Academic Journal of Interdisciplinary Studies, 2(2), 201-210.
Akanmu, M. A., & Fajemidagba, M. O. (2012). Guided-discovery learning strategy and senior school students performance in mathematics in Ejigbo, Nigeria. Journal of Education and Practice, 4(12), 82-89
Alexakis, G., Panagiotakis, S., Fragkakis, A., Markakis, E., & Vassilakis, K.(2019). Control of smart home operations using natural language processing,　voice recognition and IoT technologies in a multi-tier architecture. Designs, 3(3), 32-49.
Aljaser, A. M. (2017). The Effectiveness of Electronic Mind Maps in Developing Academic Achievement and the Attitude towards Learning English among Primary School Students. International Education Studies, 10, 80-95.
Al Naqbi, S. (2011), "The use of mind mapping to develop writing skills in UAE schools", Education, Business and Society: Contemporary Middle Eastern Issues, 4(2), 120-133.
Aluja-Banet, T., Sancho, M. R., & Vukic, I. (2019). Measuring motivation from the virtual learning environment in secondary education. Journal of Computational Science, 36, 100629.
Alves, R., & Alves, H. N. (2011). The faunal drugstore: Animal-based remedies used in traditional medicines in Latin America. Journal of ethnobiology and ethnomedicine, 7(1), 1-43.
Anderson, A., & Valero, L.(2020). Supporting academic vocabulary and social-emotional skills of students with learning disabilities through an arts-integrated social studies approach. Teaching Exceptional Children, 53(2), 150-162.
Angeletti, L. R., Agrimi, U. M. I. J. E. R. T. O., French, D., Curia, C., & Mariani-Costantini, R.(1992). Healing rituals and sacred serpents. The Lancet, 340, 223-225.
Armstrong, S. J., Cools, E., & Sadler‐Smith, E. (2012). Role of cognitive styles in business and management: Reviewing 40 years of research. International Journal of Management Reviews, 14(3), 238-262.
Arnone, M. P., Small, R. v., Chauncey, S. A., & McKenna, H. P. (2011). Curiosity, interest, and engagement in technology-pervasive learning environments: A new research agenda. Educational Technology Research and Development, 59(2), 181–198.
Astriani, D., Susilo, H., Suwono, H., Lukiati, B., & Purnomo, A. (2020). Mind mapping in learning models: A tool to improve student metacognitive skills. International Journal of Emerging Technologies in Learning, 15(6), 4-17.
Ayres, P. (2006). Impact of reducing intrinsic cognitive load on learning in a mathematical domain. Applied Cognitive Psychology: The Official Journal of the Society for Applied Research in Memory and Cognition, 20(3), 287-298.
Bandura, A., Pajares, F., & Urdan, T. (2006). Self-efficacy beliefs of adolescents. Guide for constructing self-efficacy scales, 5, 307-337.
Baraka, K., Alves-Oliveira, P., & Ribeiro, T. (2020). An extended framework for characterizing social robots. In Human-robot interaction(pp. 21-64). Springer, Cham.
Bartlett, C., Marshall, M., & Marshall, A. (2012). Two-eyed seeing and other lessons learned within a co-learning journey of bringing together indigenous and mainstream knowledges and ways of knowing. Journal of Environmental Studies and Sciences, 2, 331-340.
Beggan, A. (2020). Digital learning ecosystems: Discussing the outcomes of a principle led investigation into an alternative VLE. Journal of Higher Education Research, 3(1), 1-15.
Benotti, L., Martnez, M. C., & Schapachnik, F. (2017). A tool for introducing computer science with automatic formative assessment. IEEE Transactions on Learning Technologies, 11(2), 179-192.
Bii, P. (2013). Chatbot technology: A possible means of unlocking student potential to learn how to learn. Educational Research, 4(2), 218-221.
Bii, P. K., Too, J. K., & Mukwa, C. W. (2018). Teacher attitude towards use of Chatbots in routine teaching. Universal Journal of Educational Research, 6(7), 1586-1597.
Biktimirov, E. N., & Nilson, L. B. (2006). Show them the money: Using mind mapping in the introductory finance course. Journal of Financial Education, 32, 72-86.
Blayney, P., Kalyuga, S., & Sweller, J. (2015b). Using cognitive load theory to tailor instruction to levels of accounting students’ expertise. Educational Technology & Society, 18(4), 199–210.
Boland Jr, R. J., Tenkasi, R. V., & Te'Eni, D. (1994). Designing information technology to support distributed cognition. Organization science, 5(3), 456-475.
Boley, D. A. (2008). Use of premade mind maps to enhance simulation learning. Nurse Educator, 33(5), 220-223.
Borrego, M., & Newswander, L. K. (2010). Definitions of interdisciplinary research: Toward graduate-level interdisciplinary learning outcomes. The Review of Higher Education, 34(1), 61-84.
Budd, J. W. (2004). Mind maps as classroom exercises. The journal of economic education, 35(1), 35-46.
Buzan, T. (1974). Using both sides of your brain. New York: E. P. Dutton.
Buzan, T., & Buzan, B. (2000). The mind map book. London: BBC Books.
Camburn, B., Arlitt, R., Anderson, D., Sanaei, R., Raviselam, S., Jensen, D., & Wood, K. L. (2020). Computer-aided mind map generation via crowdsourcing and machine learning. Research in Engineering Design, 31(4), 383-409.
Canzano, D., & Grimaldi, M. (2012). An integrated framework to implement a knowledge management programme: The role of technological tools and techniques. International Journal of Intelligent Enterprise, 1(3-4), 233-247.
Car, L. T., Dhinagaran, D. A., Kyaw, B. M., Kowatsch, T., Joty, S., Theng, Y. L., & Atun, R. （2020）. Conversational agents in health care: Scoping review and conceptual analysis. Journal of Medical Internet Research, 22(8), e17158.
Chandra, Y. W., & Suyanto, S. (2019). Indonesian chatbot of university admission using a question answering system based on sequence-to-sequence model. Procedia Computer Science, 157, 367-374.
Chen, A. Y., Mashhadi, A., Ang, D., & Harkrider, N. (1999). Cultural issues in the design of technology‐ enhanced learning systems. British Journal of Educational Technology, 30(3), 217-230.
Chen, C. H. (2020). Impacts of augmented reality and a digital game on students’ science learning with reflection prompts in multimedia learning. Educational Technology Research and Development, 68(6), 3057-3076.
Chettiparamb, A. (2007). Interdisciplinarity: a literature review. HEA Interdisciplinary Teaching and Learning Group, Centre for Languages, Linguistics and Area Studies, University of Southampton.
Choi, Y., & Lee, H. (2022). Psychometric Properties for Multidimensional Cognitive Load Scale in an E-Learning Environment. International Journal of Environmental Research and Public Health, 19(10), 5822.
Chong, T., Yu, T., Keeling, D. I., & de Ruyter, K. (2021). AI-chatbots on the services frontline addressing the challenges and opportunities of agency. Journal of Retailing and Consumer Services, 63, 102735.
Chu, H. C. (2014). Potential negative effects of mobile learning on students’ learning achievement and cognitive load—a format assessment perspective. Educational Technology & Society, 17(1), 332–344.
Ciechanowski, L., Przegalinska, A., Magnuski, M., & Gloor, P.(2019). In the shades of the uncanny valley: An experimental study of human–chatbot interaction. Future Generation Computer Systems, 92, 539-548.
Clark, H. and Brennan, S. (1991). Grounding in Communication. In L.B. Resnick, J.M. Levine, and S.D. Teasley(eds.), Perspectives on Socially-Shared Cognition. APA, Washington, D.C.
Clark, H. & Schaefer, E. (1989). Collaborating on Contributions to Conversations. In R. Dietrich and C.F. Graumann (eds.), Language Processing in Social Context, Elsevier Science Publishers, North-Holland.
Corti, K., Reddy, G., Choi, E., & Gillespie, A. (2015). The researcher as experimental subject: Using self-experimentation to access experiences, understand social phenomena, and stimulate reflexivity. Integrative Psychological and Behavioral Science, 49(2), 288-308.
Dale, R. (2017). The commercial NLP landscape in 2017. Natural Language Engineering, 23(4), 641-647.
Davies, M. (2011). Concept mapping, mind mapping and argument mapping: what are the differences and do they matter?. Higher education, 62(3), 279-301.
de Jong, T., & Lazonder, A. W. (2014). The guided discovery principle in multimedia learning. In R. E. Mayer(Ed.), The Cambridge handbook of multimedia learning(pp. 371-390). New York, NY: Cambridge University Press.
de Jong, T., Lazonder, A., Pedaste, M., & Zacharia, Z. (2018). Simulations, games, and modeling tools for learning. In International handbook of the learning sciences(pp. 256-266). Routledge.
Daschuk, J. W. (2013). Clearing the plains: Disease, politics of starvation, and the loss of Aboriginal life(Vol. 65). University of Regina Press.
Delgado, C., Romero, R., Espinoza, R. V., Trigozo, M., & Correa, R. (2019). Rhynchophorus palmarum used in Traditional Medicine in the Peruvian Amazon. Ethnobiology Letters, 10(1), 120-128.
Dimoulas, C., Veglis, A. A., & Kalliris, G. (2018). Semantically enhanced authoring of shared media. In Encyclopedia of Information Science and Technology, Fourth Edition (pp. 6476-6487). IGI Global.
Dix, A. (2020). Traditional statistics. In Statistics for HCI (pp. 61-68). Springer, Cham.
Elmaadaway, M. A. N.(2017). The effects of a flipped classroom approach on class engagement and skill performance in a blackboard course. British Journal of Educational Technology, 49, 479-491.
Eppler, M. J. (2006). A comparison between concept maps, mind maps, conceptual diagrams, and visual metaphors as complementary tools for knowledge construction and sharing. Information visualization, 5(3), 202-210.
Eriksson, L. T., & Hauer, A. M. (2004). Mind map marketing: A creative approach in developing marketing skills. Journal of Marketing Education, 26(2), 174-187.
Eysink, T. H. S., De Jong, T., Berthold, K., Kollöffel, B., Opfermann, M., & Wouters, P. (2009). Learner performance in multimedia learning arrangements: An analysis across instructional approaches. American Educational Research Journal, 46, 1107–1149.
Farrand, P., Hussain, F. and Hennessy, E. (2002). The efficacy of the mind map study
technique. Medical Education 36, 5, 426–431.
Feldon, D. F., Callan, G., Juth, S., & Jeong, S. (2019). Cognitive load as motivational cost. Educational Psychology Review, 31, 319-337.
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent （Woebot）: A randomized controlled trial. JMIR Mental Health, 4(2), e7785.
Følstad, A., & Brandtzæg, P. B. (2017). Chatbots and the new world of HCI. Interactions, 24(4), 38-42.
Følstad, A., Araujo, T., Law, E. L. C., Brandtzaeg, P. B., Papadopoulos, S., Reis, L., ... & Luger, E. (2021). Future directions for chatbot research: an interdisciplinary research agenda. Computing, 103(12), 2915-2942.
Frodeman, R. (2014). Interdisciplinarity. In Sustainable Knowledge: A Theory of Interdisciplinarity(pp. 34-59). Palgrave Pivot, London.
Fryer, L. K., Nakao, K., & Thompson, A. (2019). Chatbot learning partners: Connecting learning experiences, interest and competence. Computers in Human Behavior, 93, 279-289.
Fryer, L. K., Thompson, A., Nakao, K., Howarth, M., & Gallacher, A. (2020). Supporting self-efficacy beliefs and interest as educational inputs and outcomes: Framing AI and Human partnered task experiences. Learning and Individual Differences, 80, 101850.
Garrison, D. R., & Cleveland-Innes, M. (2005). Facilitating cognitive presence in online learning: Interaction is not enough. The American Journal of Distance Education, 19(3), 133-148.
Ghanbari, A., Javadnia, F., & Abdolahi, M. (2010). Teaching of gross anatomy for students of medicine by mind map-based power point slides. Medical teacher, 32(3), 272.
Goodyear, P. (1991). A knowledge-based approach to supporting the use of simulation programs. Computers & Education, 16(1), 99-103.
Griffiths, M. D., & Kuss, D. (2017). Adolescent social media addiction(revisited). Education and Health, 35(3), 49-52.
Guttorm, G. (2012). Duodji: A new step for art education. International Journal of Art & Design Education, 31(2), 180-190.
Han, S., & Lee, M. K. (2022). FAQ chatbot and inclusive learning in massive open online courses. Computers & Education, 179, 104395.
Hariyadi, S., Corebima, A. D., & Zubaidah, S. (2018). Contribution of mind mapping, summarizing, and questioning in the RQA learning model to genetic learning outcomes. Journal of Turkish Science Education, 15(1), 80-88.
Haynes, C. (2002). Innovations in Interdisciplinary Teaching. American Council on Education/Oryx Press Series on Higher Education. Oryx Press, 88 Post Road West, Westport, CT 06881-5007.
Hennessy, S., Wishart, J., Whitelock, D., Deaney, R., Brawn, R., La Velle, L., ... & Winterbottom, M. (2007). Pedagogical approaches for technology-integrated science teaching. Computers & Education, 48(1), 137-152.
Heinrich, M., Ankli, A., Frei, B., Weimann, C., & Sticher, O. (1998). Medicinal plants in Mexico: Healers' consensus and cultural importance. Social science & medicine, 47(11), 1859-1871.
Hew, K. F., Huang, W., Du, J., & Jia, C. (2023). Using chatbots to support student goal setting and social presence in fully online activities: learner engagement and perceptions. Journal of Computing in Higher Education, 35(1), 40-68.
Hill, J., Ford, W. R., & Farreras, I. G. (2015). Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations. Computers in human behavior, 49, 245-250.
Hiremath, G., Hajare, A., Bhosale, P., Nanaware, R., & Wagh, K. S. (2018). Chatbot for education system. International Journal of Advance Research, Ideas and Innovations in Technology, 4(3), 37-43.
Holliday, W.G. (2000). Text Book Illustration: Fact or Filler? Journal Science Teacher, 57(9), 27-29.
Horne-Moyer, H. L., Moyer, B. H., Messer, D. C., & Messer, E. S. (2014). The use of electronic games in therapy: A review with clinical implications. Current Psychiatry Reports, 16(12), 1-9.
Hsu, T. C. (2017). Learning English with augmented reality: Do learning styles matter?. Computers & Education, 106, 137-149.
Huang, P. (2021). Big data application in exchange rate financial prediction platform based on FPGA and human-computer interaction. Microprocessors and Microsystems, 80, 103626.
Huang, W., Hew, K. F., & Fryer, L. K. (2022). Chatbots for language learning—Are they really useful? A systematic review of chatbot‐ supported language learning. Journal of Computer Assisted Learning, 38(1), 237-257.
Huang, Y. M., & Chiu, P. S. (2015). The effectiveness of the meaningful learning-based evaluation for different achieving students in a ubiquitous learning context. Computers & Education, 87, 243-253.
Hwang, G. J., Chiu, M. C., Hsia, L. H., & Chu, H. C. (2022). Promoting art appreciation performances and behaviors in effective and joyful contexts: A two-tier test-based digital gaming approach. Computers & Education, 194, 104706.
Hwang, G. J., Yang, T. C., Tsai, C. C., & Yang, Stephen J. H. (2009). A context-aware ubiquitous learning environment for conducting complex science experiments. Computers & Education, 53(2), 402-413.
IBM. (2022). IBM Watson Assistant. Retrieved from https://www.ibm.com/downloads/cas/ZDJDQLGV.
Iku-Silan, Hwang & Chen. (2023). Decision-guided chatbots and cognitive styles in interdisciplinary learning. Computers and Education, 201, 104812.
Ivanitskaya, L., Clark, D., Montgomery, G., & Primeau, R. (2002). Interdisciplinary learning: Process and outcomes. Innovative Higher Education, 27(2), 95-111.
Jackson, K. M., & Trochim, W. M. (2002). Concept mapping as an alternative approach for the analysis of open-ended survey responses. Organizational research methods, 5(4), 307-336.
Jamaludin, R., & Osman, S. Z. (2014). The use of a flipped classroom to enhance engagement and promote active learning. Journal of Education and Practice, 5, 124–131.
Karahasanović, A., Hollebeek, L. D., Chasanidou, D., & Gurau, C. (2019). Temporality of customer engagement in service innovation: A theoretical model. In Handbook of research on customer engagement. Edward Elgar Publishing.
Kashef, M., Visvizi, A., & Troisi, O. (2021). Smart city as a smart service system: Human-computer interaction and smart city surveillance systems. Computers in Human Behavior, 124, 106923.
Katona, J. (2021). A review of human–computer interaction and virtual reality research fields in cognitive InfoCommunications. Applied Sciences, 11(6), 2646.
Kendie, F. A., Mekuriaw, S. A., & Dagnew, M. A. (2018). Ethnozoological study of traditional medicinal appreciation of animals and their products among the indigenous people of Metema Woreda, North-Western Ethiopia. Journal of ethnobiology and ethnomedicine, 14, 1-12.
Kent, D. (2021). Voice-user interfaces for TESOL: Potential and receptiveness among native and non-native English speaking instructors. Language Learning & Technology, 25(3), 27-39.
Khan, A., Ranka, S., Khakare, C., & Karve, S. (2019). NEEV: An education informational chatbot. International Research Journal of Engineering and Technology, 6(4), 492-495.
Khanna, A., Pandey, B., Vashishta, K., Kalia, K., Pradeepkumar, B., & Das, T. (2015). A Study of Today’s A.I. through Chatbots and Rediscovery of Machine Intelligence. International Journal of U- and e-Service, Science and Technology, 8(7), 277–284.
Kim, H., Yang, H., Shin, D., & Lee, J. H. (2022). Design principles and architecture of a second language learning chatbot. Language Learning & Technology, 26(1), 1-18.
Klein, J. T. (2005). Integrative learning and interdisciplinary studies. Peer Review, 7(4), 8-10.
Klepsch, M., Schmitz, F., & Seufert, T. (2017). Development and validation of two instruments measuring intrinsic, extraneous, and germane cognitive load. Frontiers in psychology, 8, 1997.
Kotcherlakota, S., Zimmerman, L., & Berger, A. M. (2013). Developing scholarly thinking using mind maps in graduate nursing education. Nurse educator, 27(6), 252-255.
Latulippe, N., & Klenk, N. (2020). Making room and moving over: knowledge co-production, Indigenous knowledge sovereignty and the politics of global environmental change decision-making. Current Opinion in Environmental Sustainability, 42, 7-14.
Lawlor, D. A., Dickel, C. D., Hauswirth, W. W., & Parham, P. (1991). Ancient HLA genes from 7,500-year-old archaeological remains. Nature, 349(6312), 785-788.
Leroy, N., & Bressoux, P. (2016). Does amotivation matter more than motivation in predicting mathematics learning gains? A longitudinal study of sixth-grade students in France. Contemporary Educational Psychology, 44, 41-53.
Lev, E. (2003). Traditional healing with animals(zootherapy): medieval to present-day Levantine practice. Journal of ethnopharmacology, 85(1), 107-118.
Li, J. (2017). A synthetic research on the multimedia data encryption based mobile computing security enhancement model and multi-channel mobile human computer interaction framework. Multimedia Tools and Applications, 76(16), 16963-16987.
Lin, C. J., & Mubarok, H. (2021). Learning analytics for investigating the mind map-guided AI chatbot approach in an EFL flipped speaking classroom. Educational Technology & Society, 24(4), 16-35.
Lin, M. P. C., & Chang, D. (2020). Enhancing post-secondary writers’ writing skills with a chatbot. Journal of Educational Technology & Society, 23(1), 78-92.
Li-Shu, L., & Pei-Fen, W. (2019). A Study on the Interdisciplinary Teaching Process of the Information Technology Applications on Cultural and Creative Product Design. International Journal of Information and Education Technology, 9(1). 16-20.
Long, J., Cummaford, S., & Stork, A. (2022). Toward Engineering Design Principles for HCI. Synthesis Lectures on Human-Centered Informatics, 15(2), i-202.
Makhachashvili, R., & Semenist, I. V. (2021). Interdisciplinarity and Universality of Foreign Languages Education. Journal of Systemics, Cybernetics and Informatics, 19(9), 7-17.
Malki, Z., & Atlam, E. (2021). Graduate Students and Companies Web Based E-Recruitment System. Journal of Computer and Communications, 9(9), 71-84.
Marioni, C., Soriano Ramirez, M., Gaemperli, M., Kenel, A., & Krey, M. (2023). Conversational AI–virtual assistant & chatbot at Sika Ltd.: a case sudy from the chemical industry. International Journal of Machine Learning, 13(1), 39-47.
Martens, R., Bastiaens, T., & Kirschner, P. A. (2007). New learning design in distance education: The impact on student perception and motivation. Distance education, 28(1), 81-93.
Martin, A. J., & Evans, P. (2019). Load reduction instruction (LRI): 14Sequencing explicit instruction and guided discovery to enhance students’ motivation, engagement, learning, and achievement. In Advances in cognitive load theory (pp. 15-29). Routledge.
Mayer, R. E. (2014). Multimedia instruction. Handbook of research on educational communications and technology, 385-399.
McAuley, E., Duncan, T., & Tammen, V. V. (1989). Psychometric properties of the Intrinsic Motivation Inventory in a competitive sport setting: A confirmatory factor analysis. Research quarterly for exercise and sport, 60(1), 48-58.
McClain, A. (1987). Improving Lectures: Challenging Both Sides of the Brain. Journal of Optometric Education, 13(1), 18-20.
McCord, M. C., Murdock, J. W., & Boguraev, B. K. (2012). Deep parsing in Watson. IBM Journal of research and development, 56(3.4), 3:1 - 3:15.
McKivett, A., Hudson, J. N., McDermott, D., & Paul, D. (2020). Two‐eyed seeing: A useful gaze in Indigenous medical education research. Medical Education, 54(3), 217-224.
Melki, A. (2021). Interdisciplinary computing: An epistemological examination and proposed PLOs. European Journal of Interdisciplinary Studies, 7(1), 36-47.
Mento, A.J., Martinelli, P. and Jones, R.M. (1999), "Mind mapping in executive education: applications and outcomes", Journal of Management Development, 18 (4), 390-416.
Michie, M., Hogue, M., & Rioux, J. (2018). The application of both-ways and two-eyed seeing pedagogy: Reflections on engaging and teaching science to post-secondary Indigenous students. Research in Science Education, 48(6), 1205-1220.
Mills, K. A., & Doyle, K. (2019). Visual arts: a multimodal language for Indigenous education. Language and Education, 33(6), 521-543.
Msimanga, A., & Lelliott, A. (2014). Talking science in multilingual contexts in South Africa: Possibilities and challenges for engagement in learners home languages in high school classrooms. International Journal of Science Education, 36(7), 1159-1183.
Nettleship, J. (1992). Active Learning in Economics: Mind Maps and Wall Charts. Economics, 28(118), 69-71.
Neumann, A. T., Arndt, T., Köbis, L., Meissner, R., Martin, A., de Lange, P., ... & Wollersheim, H. W. (2021). Chatbots as a tool to scale mentoring processes: Individually supporting self-study in higher education. Frontiers in Artificial Intelligence, 4, 668220.
Nguyen, H. D., Tran, T. V., Pham, X. T., Huynh, A. T., & Do, N. V. (2021). Ontology-based integration of knowledge base for building an intelligent searching chatbot. Sensors and Materials, 33(9), 3101-3123.
Nißen, M., Selimi, D., Janssen, A., Cardona, D. R., Breitner, M. H., Kowatsch, T., & von Wangenheim, F. (2022). See you soon again, chatbot? A design taxonomy to characterize user-chatbot relationships with different time horizons. Computers in Human Behavior, 127, 107043.
Nielsen, J. (1994). Usability laboratories. Behaviour & Information Technology, 13(1-2), 3-8.
Nimavat, K., & Champaneria, T. (2017). Chatbots: An overview types, architecture, tools and future possibilities. International Journal for Scientific Research & Development, 5(7), 1019-1024.
Okonkwo, C. W., & Ade-Ibijola, A. (2020). Python-Bot: A Chatbot for Teaching Python Programming. Engineering Letters, 29(1).
Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: A systematic review. Computers and Education: Artificial Intelligence, 2, 100033.
OpenAI. (2022). Introducing ChatGPT. Retrieved from https://openai.com/blog/chatgpt/
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., ... & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730-27744.
Pellas, N., Kazanidis, I., & Palaigeorgiou, G. (2020). A systematic literature review of mixed reality environments in K-12 education. Education and Information Technologies, 25(4), 2481-2520.
Paas, F., & Merriënboer, J. (1994). Instructional control of cognitive load in the training
of complex cognitive tasks. Educational Psychology Review, 6(4), 351-371.
Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38, 1-4.
Pawluk, R.R., Sandor, J. A. & J. A. Tabor, (1992). The role of indigenous soil knowledge in agricultural development. Journal of Soil and Water Conservation 47(4), 298-302.
Pereira, J., Fernández-Raga, M., Osuna-Acedo, S., Roura-Redondo, M., Almazán-López, O., & Buldón-Olalla, A. (2019). Promoting learners’ voice productions using chatbots as a tool for improving the learning process in a MOOC. Technology, Knowledge and Learning, 24(4), 545-565.
Permatasari, D. A., & Maharani, D. A. (2021). Combination of Natural Language Understanding and Reinforcement Learning for Booking Bot. Journal of Electrical, Electronic, Information, and Communication Technology, 3(1), 12-17.
Pérez, J. Q., Daradoumis, T., & Puig, J. M. M. (2020). Rediscovering the use of chatbots in education: A systematic literature review. Computer Applications in Engineering Education, 28(6), 1549-1565.
Pölzer, S., & Miesenberger, K. (2015). 2D presentation techniques of mind-maps for blind meeting participants. In Assistive Technology (pp. 533-538). IOS Press.
Pressley, M., Van Etten, S., Yokoi, L., Freebem, G., & Van Meter, P. (1998). The metacognition of college studentship: A grounded theory approach. In Metacognition in educational theory and practice (pp. 361-380). Routledge.
Qaffas, A. A. (2019). Improvement of Chatbots semantics using wit. ai and word sequence kernel: Education Chatbot as a case study. International journal of modern education and computer science, 11(3), 16.
Ramanujan, D., Zhou, N., & Ramani, K. (2019). Integrating environmental sustainability in undergraduate mechanical engineering courses using guided discovery instruction. Journal of Cleaner Production, 207, 190-203.
Röhrig, C., & Heß, D. (2019). OmniMan: A Mobile Assistive Robot for Intralogistics Applications. Engineering Letters, 27(4). 30-45.
Rosner, F. (1992). Pigeons as a remedy(segulah)for jaundice. NY State J Med, 92(5), 189-192.
Rieber, L. P.(2005). Multimedia learning in games, simulations, and microworlds. The Cambridge handbook of multimedia learning, 549-567.
Rionanda, L. S., Farida, F., Putra, F. G., Damayanti, E., & Pradana, K. C. (2022). ICT-Based Lajur Bata Game Media Using Guided Discovery Method on Flat-sided Space Geometry Subject. Journal Corner of Education, Linguistics, and Literature, 1(4), 235-248.
Rubinacci, F., Ponticorvo, M., Passariello, R., & Miglino, O. (2017). Robotics for soft skills training. Research on Education and Media, 9(2), 20-25.
Ryan, R M and E. L. Deci. (2000). ‘Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions’. Contemp. Educ. Psychol., 25(1), 54–67.
Sampayo-Vargas, S., Cope, C. J., He, Z., & Byrne, G. J. (2013). The effectiveness of adaptive difficulty adjustments on students' motivation and learning in an educational computer game. Computers & Education, 69, 452-462.
Seow, S. C. (2005). Information theoretic models of HCI: A comparison of the Hick-Hyman law and Fitts' law. Human-computer interaction, 20(3), 315-352.
Shepherd, R. (2010). If these walls could talk: Reflective Practice in Addiction Studies among undergraduates in New Zealand. International journal of mental health and addiction, 8(4), 583-594.
Shawar, B. A., & Atwell, E. S. (2005). Using corpora in machine-learning chatbot systems. International journal of corpus linguistics, 10(4), 489-516.
Shi, Y. S., & Tsai, C. Y. (2022). Fostering vocabulary learning: mind mapping app enhances performances of EFL learners. Computer Assisted Language Learning, 35(9), 1-47.
Shi, Y., Tong, M., & Long, T. (2021). Investigating relationships among blended synchronous learning environments, students’ motivation, and cognitive engagement: A mixed methods study. Computers & Education, 168, 104193.
Shneiderman, B., & Plaisant, C. (2010). Designing the user interface: Strategies for effective human-computer interaction. Pearson Education India.
Shrager, J., & Klahr, D.(1986). Instructionless learning about a complex device: the paradigm and observations. International Journal of Man-Machine Studies, 25(2), 153-189.
Simonova, I. (2015). E-learning in mind maps of Czech and Kazakhstan university students. Procedia-Social and Behavioral Sciences, 171, 1229-1234.
Sinha, S., Basak, S., Dey, Y., & Mondal, A. (2020). An educational Chatbot for answering queries. In Emerging technology in modelling and graphics (pp. 55-60). Springer, Singapore.
Smutny, P., & Schreiberova, P. (2020). Chatbots for learning: A review of educational chatbots for the Facebook Messenger. Computers & Education, 151, 103862.
Stentoft, D. (2017). From saying to doing interdisciplinary learning: Is problem-based learning the answer?. Active Learning in Higher Education, 18(1), 51-61.
Sundar, S. S., Bellur, S., Oh, J., Jia, H., & Kim, H. S. (2016). Theoretical importance of contingency in human-computer interaction: Effects of message interactivity on user engagement. Communication Research, 43(5), 595-625.
Sundar, S. S., Kalyanaraman, S., & Brown, J. (2003). Explicating web site interactivity: Impression formation effects in political campaign sites. Communication research, 30(1), 30-59.
Su, Y. S., Shao, M., & Zhao, L. (2022). Effect of mind mapping on creative thinking of children in scratch visual programming education. Journal of Educational Computing Research, 60(4), 906-929.
Tang, R., & Gavin, M. C. (2016). A classification of threats to traditional ecological knowledge and conservation responses. Conservation and Society, 14(1), 57-70.
Te'eni, D., Carey, J., & Zhang, P. (2007). Human-computer interaction: Developing effective organizational information systems. John Wiley & Sons, Inc.
Thomas, H. (2020). Critical literature review on chatbots in education. IJTSRD, 4(6), 786-788.
Trochim, W. M. (1989). An introduction to concept mapping for planning and evaluation. Evaluation and program planning, 12(1), 1-16.
Turing, A. M. (2009). Computing machinery and intelligence (pp. 23-65). Springer Netherlands.
Turing, A. M., & Haugeland, J. (1950). Computing machinery and intelligence (pp. 29-56). Cambridge, MA: MIT Press.
Van Merriënboer, J. J., & Sweller, J. (2010). Cognitive load theory in health professional education: design principles and strategies. Medical education, 44(1), 85-93.
Vanneste, P., Raes, A., Morton, J., Bombeke, K., Van Acker, B. B., Larmuseau, C., ... & Van den Noortgate, W. (2021). Towards measuring cognitive load through multimodal physiological data. Cognition, Technology & Work, 23, 567-585.
Varey, C., & Kahneman, D. (1992). Experiences extended across time: Evaluation of moments and episodes. Journal of Behavioral Decision Making, 5(3), 169-185.
Vázquez-Cano, E., Mengual-Andrés, S., & López-Meneses, E. (2021). Chatbot to improve learning punctuation in Spanish and to enhance open and flexible learning environments. International Journal of Educational Technology in Higher Education, 18(1), 1-20.
Veglis, A., & Maniou, T. A. (2018). The mediated data model of communication flow: Big data and data journalism. KOME: An International Journal of Pure Communication Inquiry, 6(2), 32-43.
Vezzetti, E., Moos, S., & Kretli, S. (2011). A product lifecycle management methodology for supporting knowledge reuse in the consumer packaged goods domain. Computer-Aided Design, 43(12), 1902-1911.
Vollet, J. W., Kindermann, T. A., & Skinner, E. A. (2017). In peer matters, teachers matter: Peer group influences on students’ engagement depend on teacher involvement. Journal of Educational Psychology, 109(5), 635.
Walker, M. (2009). An investigation into written comments on assignments: do students find them usable?. Assessment & Evaluation in Higher Education, 34(1), 67-78.
Wallace, R. S. (2009). The anatomy of ALICE. In Parsing the turing test (pp. 181-210). Springer, Dordrecht.
Williams, D. M. (2010). Outcome expectancy and self-efficacy: Theoretical implications of an unresolved contradiction. Personality and Social Psychology Review, 14(4), 417-425.
Williams, D. M., & Rhodes, R. E. (2016). The confounded self-efficacy construct: conceptual analysis and recommendations for future research. Health psychology review, 10(2), 113-128.
Wiltse, L. (2015). Not just ‘sunny days’: Aboriginal students connect out‐of‐school literacy resources with school literacy practices. Literacy, 49(2), 60-68.
Weatherdon, M. S. (2022). Religion, animals, and indigenous traditions. Religions, 13(7), 654.
Weaver, M. R. (2006). Do students value feedback? Student perceptions of tutors’ written responses. Assessment & Evaluation in Higher Education, 31(3), 379-394.
Wollny, S., Schneider, J., Di Mitri, D., Weidlich, J., Rittberger, M., & Drachsler, H. (2021). Are we there yet?-A systematic literature review on chatbots in education. Frontiers in artificial intelligence, 4, 654924.
Wu, B., Hu, Y., & Wang, M. (2019). Scaffolding design thinking in online STEM preservice teacher training. British Journal of Educational Technology, 50(5), 2271-2287.
Wu, H. Z., & Wu, Q. T. (2020). Impact of mind mapping on the critical thinking ability of clinical nursing students and teaching application. Journal of International Medical Research, 48(3), 0300060519893225.
Xia, Q., Chiu, T. K., Chai, C. S., & Xie, K. (2023). The mediating effects of needs satisfaction on the relationships between prior knowledge and self‐regulated learning through artificial intelligence chatbot. British Journal of Educational Technology,54(3), 1-15.
Xia, Q., Chiu, T. K., Lee, M., Sanusi, I. T., Dai, Y., & Chai, C. S. (2022). A self-determination theory (SDT) design approach for inclusive and diverse artificial intelligence (AI) education. Computers & Education, 189, 104582.
Xu, B., & Zhuang, Z. (2022). Survey on psychotherapy chatbots. Concurrency and Computation: Practice and Experience, 34(7), e6170.
Xu, L., Prain, V., & Speldewinde, C. (2021). Challenges in designing and assessing student interdisciplinary learning of optics using a representation construction approach. International Journal of Science Education, 43(6), 844-867.
Xu, W., Dainoff, M. J., Ge, L., & Gao, Z. (2023). Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI. International Journal of Human–Computer Interaction, 39(3), 494-518.
Yin, J., Goh, T. T., Yang, B., & Xiaobin, Y. (2021). Conversation technology with micro-learning: The impact of chatbot-based learning on students’ learning motivation and performance. Journal of Educational Computing Research, 59(1), 154-177.
Young, J. Q., Van Merrienboer, J., Durning, S., & Ten Cate, O. (2014). Cognitive load theory: implications for medical education: AMEE Guide No. 86. Medical teacher, 36(5), 371-384.
Young, M. S., Brookhuis, K. A., Wickens, C. D., & Hancock, P. A. (2015). State of science: mental workload in ergonomics. Ergonomics, 58(1), 1-17.
Zhao, L., Han, G., Li, Z., & Shu, L. (2020). Intelligent digital twin-based software-defined vehicular networks. IEEE Network, 34(5), 178-184.
Zhuang, Q., Gan, S., & Zhang, L. (2022). Human-computer interaction based health diagnostics using ResNet34 for tongue image classification. Computer Methods and Programs in Biomedicine, 226, 107096.