RAISE: Robotics & AI to improve STEM and social skills for elementary school students
Open accessAbstract
The authors present the design and implementation of an exploratory virtual learning environment that assists children with autism (ASD) in learning science, technology, engineering, and mathematics (STEM) skills along with improving social-emotional and communication skills. The primary contribution of this exploratory research is how educational research informs technological advances in triggering a virtual AI companion (AIC) for children in need of social-emotional and communication skills development. The AIC adapts to students’ varying levels of needed support. This project began by using puppetry control (human-in-the-loop) of the AIC, assisting students with ASD in learning basic coding, practicing their social skills with the AIC, and attaining emotional recognition and regulation skills for effective communication and learning. The student is given the challenge to program a robot, Dash™, to move in a square. Based on observed behaviors, the puppeteer controls the virtual agent’s actions to support the student in coding the robot. The virtual agent’s actions that inform the development of the AIC include speech, facial expressions, gestures, respiration, and heart color changes coded to indicate emotional state. The paper provides exploratory findings of the first 2 years of this 5-year scaling-up research study. The outcomes discussed align with a common approach of research design used for students with disabilities, called single case study research. This type of design does not involve random control trial research; instead, the student acts as her or his own control subject. Students with ASD have substantial individual differences in their social skill deficits, behaviors, communications, and learning needs, which vary greatly from the norm and from other individuals identified with this disability. Therefore, findings are reported as changes within subjects instead of across subjects. While these exploratory observations serve as a basis for longer term research on a larger population, this paper focuses less on student learning and more on evolving technology in AIC and supporting students with ASD in STEM environments.
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AI tools using NLP can alleviate the need for reinforcement from adults while helping increase self-regulation skills.
“These types of AI tools using NLP can alleviate the need for reinforcement from adults, while helping increase self-regulation skills (Hughes et al., 2022).”
From The Future of Artificial Intelligence in Special Education Technology (2023) · cites this paper as Hughes et al., 2022
The Introduction (section 1) says the AI companion is intended to support students’ self-regulation instead of requiring constant monitoring by a teacher or instructional assistant. But the paper does not establish this as an achieved effect of an NLP tool: section 4.2.3 says the classroom phase still uses a human in the loop and its language patterns have yet to be analyzed for fully AI-driven behavior. (LLM, conf=0.87)
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References 48 · Cited by 47
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- Abualsamid, A., and Hughes, C. E. (2018). "Modeling augmentative communication with Amazon lex and polly," in Advances in usability, user experience and assistive technology. AHFE 2018. Advances in intelligent systems and computing. Editors T. Ahram and C. Falcão (Cham: Springer), 794, 871-879.
- Acar, C., Tekin-İftar, E., and Yıkmış, A. (2016). Effects of mother-delivered social stories and video modeling in teaching social skills to children with autism spectrum disorders. J. Spec. Educ. 50 (4), 215-226. doi:10.1177/ 0022466916649164 · DOI
- Ali, K., and Hughes, C. E. (2020). "Face reenactment based facial expression recognition," in Advances in visual computing. ISVC 2020. Lecture Notes in computer science 12509. Editors (Cham: Springer), 501-513.
- Ali, K., and Hughes, C. E. (2021). Facial expression recognition by using a disentangled identity-invariant expression representation. 25 th Int. Conf. Pattern Recognit. (ICPR 2020), 9460-9467. doi:10.1109/ICPR48806.2021. 941217 American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders. Available at: https://doi-org.ezproxy.frederick.edu/10.1176/appi. books.9780890425596. · DOI · Paper page
- Anderson, K., Dubiel, T., Tanaka, K., and Worsley, M. (2019). Proceedings of the 2019 international conference on multimodal interaction, 506-507. doi:10.1145/ 3340555.3358662 Chemistry pods: A mutlimodal real time and retrospective tool for the classroom. · DOI
- Bai, M., Xie, W., and Shen, L. (2019). Proceedings of 2019 IEEE international conference on image processing (ICIP), 31-35.Disentangled feature based adversarial learning for facial expression recognition.
- Baskoro, R. A. (2021). The comparison of numbered head together learning models and think pair share in terms of elementary school mathematics learning outcomes. International. J. Elem. Educ. 4 (4), 549-557. doi:10.23887/ijee.v414.32568 · DOI
- Beddiar, D. R., Nini, B., Sabokrou, M., and Hadid, A. (2020). Vision-based human activity recognition: A survey. Multimed. Tools Appl. 79 (41), 30509-30555. doi:10. 1007/s11042-020-09004-3 · DOI
- Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., and Tanaka, F. (2018). Social robots for education: A review. Sci. Robot. 3 (21), eaat5954. doi:10. 1126/scirobotics.aat5954 · DOI · Paper page
- Ben-Ari, M., and Mondada, F. (2018). "Robots and their applications," in Elements of robotics (Cham: Springer). doi:10.1007/978-3-319-62533-1_1 · DOI
- Bers, M. U. (2010). Issue editor's notes. New media and technology: Youth as content creators. New Dir. Youth Dev. 12 (2), 1-4. doi:10.1002/yd.369 · DOI
- Bulagang, A. F., Mountstephens, J., and Teo, J. (2021). Multiclass emotion prediction using heart rate and virtual reality stimuli. J. Big Data 8 (1), 12. doi:10.1186/s40537-020-00401-x · DOI
- Editors G. Bebis, R. Boyle, B. Parvin, D. Koracin, A. Ushizima, S. Chai, et al. (Cham: Springer), 540-551.
- Dieker, L. A., Straub, C., Hynes, M., Hughes, C. E., Bukaty, C., Bousfield, T., et al. (2019). Using virtual rehearsal in a simulator to impact the performance of science teachers. Int. J. Gaming Comput. Mediat. Simul. 11 (4), 1-20. doi:10.4018/IJGCMS. 2019100101 · DOI
- Donnermann, M., Schaper, P., and Lugrin, B. (2022). Social robots in applied settings: A long-term study on adaptive robotic tutors in higher education. Front. Robot. AI 9, 831633. doi:10.3389/frobt.2022.831633 · DOI
- Geist, E. (2016). Robots, programming and coding, oh my. Child. Educ. 92, 298-304. doi:10.1080/00094056.2016.1208008 · DOI
- Ginosar, S., Bar, A., Kohavi, G., Chan, C., Owens, A., and Malik, J. (2019). Learning individual styles of conversational gesture. Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 3497-3506. doi:10.1109/CVPR.2019.00361 · DOI
- Editors G. Burnett, S. Coquillard, R. VanLiere, and G. Welch (Springer LNCS), 133-146.
- Humphrey, N., and Symes, W. (2011). Peer interaction patterns among adolescents with autistic spectrum disorders (ASDs) in mainstream school settings. Autism 15 (4), 397-419. doi:10.1177/1362361310387804 Individuals with Disabilities (2004). Individuals with disabilities education improvement act. Stat 118. · DOI
- Keating, C. T., and Cook, J. L. (2020). Facial expression production and recognition in autism spectrum disorders: A shifting landscape. Child Adolesc. Psychiatric Clin. N. Am. 29 (3), 557-571. doi:10.1016/j.chc.2020.02.006 · DOI · Paper page
- Khalil, A., Ahmed, S. G., Khattak, A. M., and Al-Qirim, N. (2020). Investigating bias in facial analysis systems: A systematic review. IEEE Access 8, 130751-130761. doi:10.1109/access.2020.3006051 · DOI
- Krzeminska, A., and Hawse, S. (2020). "Mainstreaming neurodiversity for an inclusive and sustainable future workforce: Autism-spectrum employees," in Industry and higher Education: Case studies for sustainable futures (Springer), 229-261.
- Laugeson, E., Gantman, A., Kapp, S., Orenski, K., and Ellingsen, R. (2015). A randomized controlled trial to improve social skills in young adults with autism spectrum disorder: The UCLA peers program. J. Autism Dev. Disord. 45 (12), 3978-3989. doi:10.1007/s10803-015-2504-8 · DOI
- Levenson, R. W., Ekman, P., and Friesen, W. V. (1990). Voluntary facial action generates emotion-specific autonomic nervous system Activity. Psychophysiology 27, 363-384. doi:10.1111/j.1469-8986.1990.tb02330.x · DOI · Paper page
- Lin, J., Jiang, F., and Shen, R. (2018). IEEE international conference on acoustics. New York, NY, United States: Speech & Signal Processing ICASSP, 6453-6457.
- Lye, S. Y., and Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Comput. Hum. Behav. 41, 51-61. doi:10.1016/j.chb.2014.09.012 · DOI · Paper page
- Monkaresi, H., Bosch, N., Calvo, R., and D'Mello, S. (2016). Automated detection of engagement using video-based estimation of facial expressions and heart rate. IEEE Trans. Affect. Comput. 8 (1), 15-28. doi:10.1109/taffc. 2016.2515084 · DOI
- Mutluer, T., Aslan Genç, H., Özcan Morey, A., Yapici Eser, H., Ertinmaz, B., Can, M., et al. (2022).Population-based psychiatric comorbidity in children and adolescents with autism spectrum disorder: A meta-analysis, Front. Psychiatry , PMC9186340. PMID: 35693977. doi:10.3389/fpsyt.2022. 856208 · DOI
- Nezami, O. M., Dras, M., Hamey, L., Richards, D., Wan, S., and Paris, C. (2019). Joint European conference on machine learning and knowledge discovery in databases. Springer, 273-289.Automatic recognition of student engagement using deep learning and facial expression.
- Nojavanasghari, B., Baltrusaitis, T., Hughes, C. E., and Morency, L-P. (2016). EmoReact: A multimodal approach and dataset for recognizing emotional responses in children. Proc. Int. Conf. Multimodal Interact. (ICMI 2016), 137-144. doi:10.1145/2993148.2993168 · DOI
- Odom, S. L., Cox, A. W., and Brock, M. E. (2013). Implementation science, professional development, and autism spectrum disorders. Except. Child. 79 (2), 233-251. doi:10.1177/001440291307900207 · DOI
- Odom, S., Thompson, J., Hedges, S., Boyd, B., Dykstra, J., Duda, M., et al. (2015). Technology-aided interventions and instruction for adolescents with autism spectrum disorder. J. Autism Dev. Disord. 45 (12), 3805-3819. doi:10.1007/ s10803-014-2320-6 · DOI
- Ola, L., and Gullon-Scott, F. (2020). Facial emotion recognition in autistic adult females correlates with alexithymia, not autism. Autism 24 (8), 2021-2034. doi:10. 1177/1362361320932727 · DOI
- Rahayu, S., and Suningsih, A. (2018). The effects of type learning model numbered head together and think pair share. Int. J. Trends Math. Edu. Res. 1 (1), 19-21. doi:10.33122/ijtmer.v1i1. · DOI
- Rose, D. H., and Meyer, A. (2008). A practical reader in universal design for learning. Cambridge, MA: Harvard Education Press.
- Scassellati, B., Boccanfuso, L., Huang, C. M., Mademtzi, M., Qin, M., Salomons, N., et al. (2018). Improving social skills in children with ASD using a long-term, in-home social robot. Sci. Robot. 3 (21), eaat7544. doi:10. 1126/scirobotics.aat7544 · DOI
- Seraj, M., Katterfeldt, E-S., Bub, K., Autexier, S., and Drechsler, R. (2019). Scratch and google blockly: How girls' programming skills and attitudes are influenced, Proc. 19th Koli Call. Int. Conf. Comput. Educ. 19, 1-10. doi:10. 1145/3364510.3364515 · DOI
- Severson, H. H., Hill, M. W., Hope-Doolittle, J., Kratochwill, T. R., and Gresham, F. M. (2007). Proactive, early screening to detect behaviorally at-risk students: Issues, approaches, emerging innovations, and professional practices. J. Sch. Psychol. 45 (2), 193-223. doi:10.1016/j.jsp.2006.11.003 · DOI
- Shu, L., Yu, Y., Chen, W., Hua, H., Li, Q., Jin, J., et al. (2020). Wearable emotion recognition using heart rate data from a smart bracelet. Sensors 20 (3), 718. doi:10. 3390/s20030718 · DOI
- Strawhacker, A., and Bers, M. (2015). "I want my robot to look for food": Comparing Kindergartner's programming comprehension using tangible, graphic, and hybrid user interfaces. Int. J. Technol. Des. Educ. 25 (3), 293-319. doi:10.1007/ s10798-014-9287-7 · DOI
- Sullivan, F. R., and Heffernan, J. (2016). Robotic construction kits as computational manipulatives for learning in the STEM disciplines. J. Res. Technol. Educ. 48 (2), 105-128. doi:10.1080/15391523.2016.1146563 · DOI
- Syriopoulou-Delli, C. K., and Gkiolnta, E. (2022). Review of assistive technology in the training of children with autism spectrum disorders, International. J. Dev. Disabil. 68 (2), 73-85. doi:10.1080/20473869.2019. 1706333 · DOI
- Taylor, M. S. (2018). Computer programming with pre-K through first-grade students with intellectual disabilities. J. Spec. Educ. 52 (2), 78-88. doi:10.1177/ 0022466918761120 · DOI
- Taylor, M. S., Vasquez, E., and Donehower, C. (2017). Computer programming with early elementary students with Down syndrome. J. Spec. Educ. Technol. 32 (3), 149-159. doi:10.1177/0162643417704439 · DOI
- Valencia, K., Rusu, C., Quiñones, D., and Jamet, E. (2019). The impact of technology on people with autism spectrum disorder: A systematic literature review. Sensors 19 (20), 4485. doi:10.3390/s19204485 Wonder Workshop Wonder Workshop (n.d.) blockly. Retrieved from. Available at: https://home.makewonder.com/apps/blockly. · DOI
- Zhu, J., Ji, L., and Liu, C. (2019). Heart rate variability monitoring for emotion and disorders of emotion. Physiol. Meas. 40, 064004. doi:10.1088/ 1361-6579/ · DOI
- Authors
- Hughes, C. E., Dieker, L. A., Glavey, E. M., Hines, R. A., Wilkins, I., Ingraham, K., Bukaty, C. A., Ali, K., Shah, S., Murphy, J., & Taylor, M. S.
- Affiliations
- University of Central Florida (US)
- University of South Florida (US)
- Salve Regina University (US)
- Corresponding author
- Hughes, Charles E.
- ORCID
- Venue
- Frontiers in Virtual Reality
- Publisher
- Frontiers Media SA
- Published
- 2022-10-14
- Volume
- 3
- ISSN
- 2673-4192 (ISSN-L)
- OpenAlex
- W4306253206
- Cited by
- 47
- References
- 48
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 15 · 2023: 10
- FWCI
- 8.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Virtual Reality (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Directorate for Engineering (2120240 2114808 1725554)
- Institute of Education Sciences (P116S210001 H327S210005 H327S200009H)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Keywords
- autism spectrum disorder, socioemotional skills, communication skills, STEM education, programming education, robot programming, emotion recognition, emotion regulation, human-in-the-loop, elementary school students
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