Análisis mediante inteligencia artificial de las emociones del alumnado autista en la interacción social con el robot NAO

  1. Lorenzo Lledó, Gonzalo 1
  2. Lorenzo-Lledó, Alejandro 1
  3. Rodríguez-Quevedo, Angel 1
  1. 1 Universitat d'Alacant
    info

    Universitat d'Alacant

    Alicante, España

    ROR https://ror.org/05t8bcz72

Aldizkaria:
RED: revista de educación a distancia

ISSN: 1578-7680

Argitalpen urtea: 2024

Zenbakien izenburua: Generative AI, ChatGPT and Education. Consequences for Intelligent Learning and Educational Assessment.

Alea: 24

Zenbakia: 78

Mota: Artikulua

DOI: 10.6018/RED.588091 DIALNET GOOGLE SCHOLAR lock_openDIGITUM editor

Beste argitalpen batzuk: RED: revista de educación a distancia

Laburpena

Currently, technology is the most widely used tool in the development of daily life activities. The number of fields of knowledge that benefit from its versatility and application in the development of their activities is increasing. In the educational environment, it allows the generation of activities adapted to the needs of students. In recent years, robotics and artificial intelligence are the most widespread. The characteristics of these tools favour their application with students with autism spectrum disorder. Therefore, the objective of the research is the application of robotics to promote communication and social interaction in students with autism by analysing the emotions they show throughout the different activities. For this purpose, a pilot study was implemented with the NAO robot and four autistic children who developed imitation, game and social interaction activities. An automatic system based on convolutional neural networks was used to detect mood states in the interaction process. The results show that sadness, happiness and anger are the emotions most likely to occur in the participants. Therefore, it is concluded that the robot and the artificial intelligence system are a fundamental element to help express emotions in social interaction.

Erreferentzia bibliografikoak

  • Alban, A., Alhaddad, A., Al-Ali, A., Wing-Chee, S., Connor, O., Ayesh, M., Qidwai, U. & Cabibihan, J. (2023). Heart Rate as a Predictor of Challenging Behaviours among Children with Autism from Wearable Sensors in Social Robot Interactions. Robotics, 12(2), 1-13.https://doi.org/10.3390/robotics12020055
  • Alonso-Esteban, Y. & Alcantud-Marín, F. (2022). Screening, Diagnosis and Early Intervention in Autism Spectrum Disorders. Children, 9(2), 1-5. https://doi.org/10.3390/children9020153
  • Al-Nafjan, A., Alhakbani, N. & Alabdulkareem, A. (2023). Measuring Engagement in Robot-Assisted Therapy for Autistic Children. Behavioral Science, 13(8),1-16 https://doi.org/10.3390/bs13080618
  • Al-Saadi, M. & Al-Thani, D. (2023). Mobile Application to identify and recognize emotions for children with autism: A systematic review. Frontiers in Child and Adolescent Psychiatry, 2(1),1-10. https://doi.org/10.3389/frcha.2023.1118665
  • American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders(5th ed.). Arington, VA: American Psychiatric Publishing.
  • Amirova, A., Rakhymbayeva, R., Zhanatkyzy, A., Telisheva, Z. & Sandygulova, A. (2023). Effects of Parental Involvement in Robot-Assisted Autism Therapy. Journal of Autism and Developmental Disoders, 53 (1), 438-455. https://doi.org/10.1007/s10803-022-05429-x
  • Armijos, J., Quinto, E., Álvarez, L., Morocho, R. & Llerena, J. (2023). Técnicas de intervención en el trastorno del espectro autista: una revisón sistemática. Universidad y Sociedad, 15(4), 192-203.
  • Bargagna, S., Castro, E., Cecchi, F., Cioni, G., Dario, P., Dell’Omo, M., Di Lieto, M. C., Inguaggiato, E., Martinelli, A., Pecini, C., & Sgandurra, G. (2019). Educational robotics in down syndrome: A feasibility study. Technology, Knowledge and Learning, 24(2), 315–323. https://doi.org/10.1007/s10758-018-9366-z
  • Baxter P. & Jack S. (2008). Qualitative case study methodology: Study design and implementation for novice researchers. The Qualitative Report, 13(1), 544–559. https://doi.org/10.46743/2160-3715/2008.1573
  • Baykasoğlu, A., Özbel, B. K., Dudaklı, N., Subulan, K., & Şenol, M. E. (2018). Process mining based approach to performance evaluation in computer-aided examinations. Computer Applications in Engineering Education, 26(5), 1841–1861. https://doi.org/10.1002/cae.21971
  • Bharatharaj, J., Huang, L., Mohan, R., Al-Jumaily, A. and Krägeloh, C. (2017). Robot-Assisted Therapy for Learning and Social Interaction of Children with Autism Spectrum Disorder, Robotics, 6(1),1-11 https://doi.org/10.3390/robotics6010004
  • Bick, J., Fox, N., Zeanah, C. and Nelson C. (2017). Early deprivation, atypical brain development and internalizing symptoms in late childhood. Neuroscience, 07(342), 153. https://doi.org/10.1016/j.neuroscience.2015.09.026
  • Brewer, R., Biotti, F., Catmur, C., Press, C., Happé, F., Cook, R. & Bird, G. (2016). Can Neurotypical Individuals Read Autistic Facial Expressions? Atypical Production of Emotional Facial Expressions in Autism Spectrum Disorders. Autism Research, 9(2), 262-271. https://doi.org/10.1002/aur.1508
  • Brewer, N., Georgopoulus, M., Lucas, C. and Young, R. (2023). Autistic adults' perspectives on appropriate empathic responses to others' emotions. Autism Research, 16(8), 1573-1585. https://doi.org/10.1002/aur.2965
  • Brosnan, M., Good, J., Parsons, S., Yuill, N. (2019). Look up! Digital technologies for autistic people to support interaction and embodiment in the real world. Res. Autism Spectr. Disord. 58 (1), 52–53. 10.1016/j.rasd.2018.11.010
  • Brown, S. & Bebko, J. (2012). Generalization, overselectivity, and discrimination in the autism phenotype: A review.Research in Autism Spectrum Disorders, 6(2), 733–740.https://doi.org/10.1016/j.rasd.2011.10.012
  • Bussell, J. (2020). Shadowing as a Tool for Studying Political Elites. Political Analysis, 28(4), 1-18. DOI: https://doi.org/10.1017/pan.2020.14
  • Cabibihan, J.,Javed, H., Ang, M., & Aljunied, S. (2013). Why Robots? A Survey on the Roles and Benefits of Social Robots in the Therapy of Children with Autism. International Journal of Social Robotics,5(3), 593-618. https://doi.org/10.1007/s12369-013-0202-2
  • Çağataylı, M., & Çelebi, E. (2022). Estimating academic success in higher education using big five personality traits, a machine learning approach. Arab Journal Scientific Engineering, 47, 1289–1298. https://doi.org/10.1007/s13369-021-05873-4
  • Cano, S., Díaz-Arancibia, J., Arango-López, J., Libreros, J. & García, M. (2023). Design Path for a Social Robot for Emotional Communication for Children with Autism Spectrum Disorder (ASD). Sensors, 23(11), 1-24. https://doi.org/10.3390/s23115291
  • Carmona-Serrano, N., López-Belmonte, J., Cuesta-Gómez, J. & Moreno-Guerrero, A. (2020). Documentary Analysis of the Scientific Literature on Autism and Technology in Web of Science. Brain Science, 10(12), 1-17.https://doi.org/10.3390/brainsci10120985
  • Chassagnon, G., Vakalopolou, M., Paragios, N. & Revel, N. (2020).Deep learning: definition and perspectives for thoracic imaging. European Radiology, 30(1), 2021-2030. https://doi.org/10.1007/s00330-019-06564-3
  • Chu, H., Tu, Y., & Yang, K. (2022). Roles and research trends of artificial intelligence in higher education: A systematic review of the top 50 most-cited articles. Australasian Journal of Educational Technology, 38(3), 22–42. https://doi.org/10.14742/ajet.7526
  • Costa, A., Charpiot, L., Lera, F., Ziafati, P., Nazarikhorram, A., Van der Torre, L. & Steffgen, G. (2018). More attention and less repetitive and stereotyped behaviors using a robot with children with autism. In 27th IEE International Symposium on Robot and Human Interactive Communication, (pp. 534-539), IEEE:USA.
  • Conti, D., Di Nuovo, S., Trubia, G. & Di Nuovo, A. (2015). A use of robotics to stimulate imitation in children with autism spectrum disorder: a pilot study in a Clinical Setting. In Proceedings of the 24th IEEE International Symposium on Robot and Human Interactive Communication, (pp. 1-6). IEEE: USA.
  • Dawson, G., & Adams, A. (1984). Imitation and social responsiveness in autistic children. Journal of Abnormal Child Psychology, 12, 209-226.
  • De Benedictis, R., Umbrico, A., Fracasso, F., Cortellessa, G., Orlandini, A. & Cesta, A. (2023). A dichotomic approach to adaptive interaction for socially assistive robots. User Model User-Adap Inter 33, 293–331. https://doi.org/10.1007/s11257-022-09347-6
  • De Marchena, A., Eigsti, I. & Yerys, B.E (2015). Brief Report: Generalization Weaknesses in Verbally Fluent Children and Adolescents with Autism Spectrum Disorder. Journal of Autism and Developmental Disorders ,45 (1), 3370–3376. https://doi.org/10.1007/s10803-015-2478-6
  • Demuth, H. & Beale, M. (1992). Neural Network Toolbox. Natick Massachusetts: USA.
  • Dever, D. A., Azevedo, R., Cloude, E. B., & Wiedbusch, M. (2020). The impact of autonomy and types of informational text presentations in game-based environments on learning: Converging multi-channel processes data and learning outcomes. International Journal of Artificial Intelligence in Education, 30(4), 581–615. https://doi.org/10.1007/s40593-020-00215-1
  • Dijkstra, J. (2015). Social exchange: Relations and networks. Social Network Analysis and Mining, 5(1), 60. https://doi.org/10.1007/s13278-015-0301-1
  • Durkin, M., Maenner, M., Baio, J., Christensen, D., Daniel, J., Fitzgerald, R., Imm, P., Li-Ching, L., Schieve, L., Van-Naardem, K., Wingate, M., & Yeargin-Allsopp, M. (2017). Autism spectrum disorder among US children (2002–2010): socioeconomic, racial, and ethnic disparities. American Journal of Public Health, 107(11), 1818-1826. DOI: 10.2105/AJPH.2017.304032
  • Elman, J. L. (1991). Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7(2), 195-225. https://doi.org/10.1007/BF00114844
  • Fernández-Alvarado, P. & Onandia-Hinchado, I. (2022). Perfil cognitivo del trastorno del espectro autista en población infantojuvenil. una revisión sistemática. Revista de Psicología Clínica con Niños y Adolescentes, 9(3),1-14 doi: 10.21134/rpcna.2022.09.3.3
  • Feil-Seifer, D., & Mataric, M. (2005). Defining socially assistive robotics. In: 9th International Conference on Rehabilitation Robotics, ICORR 2005, pp. 465–468. IEEE: USA. https://doi.org/10.1109/ICORR.2005.1501143
  • García, R., Irarrázavala, M., López, I., Rieslea, S., Cabezas, M., Moyanoa, A., Garridoc, G., Valdez, D., Paulac, C., Rosalic, A., Cukierc, S., Montiel-Navac, C. & Rattazzic, A. (2022). Encuesta para Cuidadores de Personas del Espectro Autista en Chile. Acceso a Servicios de Salud y Educación, Satisfacción, Calidad de Vida y Estigma. Revista Chilena de Pediatría. 93 (1), 351-360.DOI: 10.32641/andespediatr.v93i3.3994
  • Giannopulu I., & Pradel, G. (2010). Multimodal interactions in free game play of children with autism and a mobile robot. NeuroRehabilitation, 27(1), 305-311. DOI: 10.3233/NRE-2010-0613
  • Graf, S., Kinshuk, Ives, C. (2010). A flexible mechanism for providing adaptivity based on learning styles in learning management systems. In proceedings of the IEEE International Conference on Advanced Learning technologies (ICALT 2010), pp.30-34. IEEE Computer Society: USA.
  • Greczek J, Kaszubski E, et al (2014) Graded cueing feedback in robot-mediated imitation practice for children with autism spectrum disorders. In Proceedings of 23rd IEEE international symposium on robot and human interactive communication, (pp. 561–566). IEEE: USA
  • Goldsmith, T. & LeBlanc, L. (2004). Use of technology in interventions for children with autism. J. Early Intensive Behav. Interv. 1, 166. https://doi.org/10.1037/h0100287
  • Goodfellow, I., Erhan, D., Carrier, P., Courville, A., Mirza, M., Hammer, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D., Zhou, Y., Ramaiah, C., Feng, F., Li, R., Wang, X., Athanasakis, D., Shawe-Taylor, J., Milakov, M., Park, J., Ionescu, R. & Bengio, Y. (2014). Challenges in representation learning: A report on three machine learning contests. Neural Networks, 64(1), 59-63. https://doi.org/10.1016/j.neunet.2014.09.005
  • Gould, J. (2009). There is more to communication than tongue placement and ‘show and tell’: Discussing communication from a speech pathology perspective. Australian Journal of Linguistics, 29(1), 59–73. https://doi.org/10.1080/07268600802516384
  • Herrero, J. & Lorenzo, G. (2020). An immersive virtual reality educational intervention on people with autism spectrum disoders (ASD) for the development of communication skills and problem solving. Education and Information Technologies, 25(1), 1689-1722. https://doi.org/10.1007/s10639-019-10050-0
  • Horwitz, E., Schoevers, R., Greaves-Lord, K., de Bildt, A. & Hartman, C. (2020). Adult manifestation of milder forms of autism spectrum disorder; autistic and non-autistic psychopathology. Journal of Autism and Developmental Disorders, 50(8). 2973-2986.doi: 10.1007/s10803-020-04403-9.
  • Howard, J. (2019). Artificial intelligence: Implications for the future of work. American Journal of Industrial Medicine, 62(11), 917-926. https://doi.org/10.1002/ajim.23037
  • Huijnen, C., Verreussel, Willen, H., Lexis, M. & Witte, L. (2021). Robot kaspar as mediator in making contact with children with autism: a pilot study. Int J Soc Robot 13(2), 237–249. https://doi.org/10.1007/s12369-020-00633-0
  • Hyman, S., Levy, S.& Myers, S. (2020). Identification, Evaluation, and Management of Children withAutism Spectrum Disorder. Pediatrics, 145(1), e20193447.doi: 10.1542/peds.2019-3447
  • Hwang, G. (2014). Definition, framework, and research issues of smart learning environments- a context-aware ubiquitous learning perspective. Smart Learning Environments, 1(4), 1-14. https://doi.org/10.1186/s40561-014-0004-5
  • Incio-Flores, F., Campuñay-Sánchez, D. & Estela-Urbina, R. (2023). Modelo de red neuronal artificial para predecir resultados académicos en la asignatura Matemática II. Revista Electrónica Educare, 27(1), 1-19. https://doi.org/10.15359/ree.27-1.14516
  • Jacques, C., Courchesne, V., Mineau, S., Dawson, M. & Mottron, L. (2022). Positive, negative, neutral—or unknown? The perceived valence of emotions expressed by young autistic children in a novel context suited to autism. Autism, 26(7), 1833-1848. https://doi.org/10.1177/13623613211068221
  • Jahromi, L., Meek, S. and Ober-Reynolds, S. (2012). Emotion regulation in the context of frustration in children with high functioning autism and their typical peers. The Journal of Child Psychology and Psychiatry, 53(12), 1250-1258. https://doi.org/10.1111/j.1469-7610.2012.02560.x
  • Kaplan, A., Kessler, T., Brill, J. & Hancock, P. (2023). Trust in Artificial Intelligence: Meta-Analytic Findings. Human Factors: The Journal of the Human Factors and Ergonomics Society, 65(2), 337-359.https://doi.org/10.1177/00187208211013988
  • Kaarbo J. & Beasley R. (1999). A practical guide to the comparative case study method in political psychology. Political Psychology, 20, 369–391.
  • Kim, A., Vaughn, S., Elbaum, B., Hughes, M., Morris, C. and Srihdar, D. (2003). Effects of Toys or Group Composition for Children With Disabilities: A Synthesis. Journal of Early Intervention, 25(3), 161-227. https://doi.org/10.1177/105381510302500304
  • Kim, S., Hirokawa, M., Matsuda, S., Funahashi, A., Suzuki, K. (2021). Smiles as a signal of prosocial behaviours toward the robot in the therapeutic settings for children with Autism spectrum disoders. Frontiers in robotics and Artificial Intelligence, 8 (1), 1-16. https://doi.org/10.3389/frobt.2021.599755
  • Kliemann, D., Dziobek, I., Hatri, A., Steimke, R., & Heekeren, H. R. (2010). Atypical reflexive gaze patterns on emotional faces in Autism Spectrum Disorders. Journal of Neuroscience, 30(37), 12281–12287. https://doi.org/10.1523/JNEUROSCI.0688-10.2010.
  • Kozima, H., Nakagawa, C. and Yasuda, Y. (2007). Children–robot interaction: a pilot study in autism therapy. Progress in Brain Research, 164 (1), 385-400. https://doi.org/10.1016/S0079-6123(07)64021-7
  • Leo, M., Del Coco, M., Carcagni, P., Distante, C., Bernava, M., Pioggia, G., Palestra, G. (2016). Automatic emotion recognition in Robot-Children interaction for ASD treatment. In: Proceedings of the IEEE International Conference on Computer Vision, 2015 (pp.537-545). IEEE: USA.
  • Lord, C., Rutter, M., DiLavore, P.,Risi, S., Gotham, K. & Bishop, S. (2012). Autism Diagnostic Observation Schedule. 2nd ed. Wester Psychological Services: Los Angeles (USA).
  • Lorenzo, G., Lledó, A., Pomares, J. & Roig-Vila, R. (2016). Design and application of an immersive virtual reality system to enhance emotional skills for children with autism spectrum disorders. Computers and Education, 98(1), 192-205.https://doi.org/10.1016/j.compedu.2016.03.018
  • Maciejewski (2020). Quasi-experimental design. Biostatistics & Epidemiology , 4(1), 38-47. https://doi.org/10.1080/24709360.2018.1477468
  • Macinska, S., Lindsay, S. & Jellema, T. (2023) Visual Attention to Dynamic Emotional Faces in Adults on the Autism Spectrum. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-023-05979-8
  • Marhon, S., Cameron, C. & Kremer, S. (2013). Recurrent Neural Network. En M. Bianchini, M. Maggini, L.C. Jain (Eds.), Handbook on Neural Information Processing, (pp.29-65). Springer: Alemania.
  • Marino, F., Chila, P., Trusso, S., Carrozza, C., Crimi, I., Failla, C., Busá, M., Bernava,G., Tartarisco, G., Vagni, D., Ruta, L. & Pioggia, G. (2020). Outcomes of a Robot-Assisted Social-Emotional Understanding Intervention for Young Children with Autism Spectrum Disorders. Journal of Autism and Developmental Disorders, 50(1), 1973-1987. https://doi.org/10.1007/s10803-019-03953-x
  • Maroto-Gómez, M., Alonso-Martín, F., Malfaz, M., Castro-González, A., Castillo, J. & Salich, M. (2023). A systematic Literature Review of Decision-Making and Control Systems for Autonomous and Social Robots. International Journal of Social Robotics, 15(1), 745-789. https://doi.org/10.1007/s12369-023-00977-3
  • Mayor-Torres, J., Medina-DeVilliers, S., Clarkson, T., Lerner, D. & Riccardi, G. (2023). Evaluation of interpretability of deep learning algorithms in EEG emotion recognition : A case study in Autism. Artificial Intelligence in Medicine, 143(1), 1-14.https://doi.org/10.1016/j.artmed.2023.102545
  • Mazefsky ,C., Herrington, J., Siegel, M., Scarpa, A., Maddox, B., Scahill, L. y White, S. (2013). The role of emotion regulation in autism spectrum disorder. The Journal of the American Academy of Child & Adolescent Pyschiatry, 52(7), 679–88. doi: 10.1016/j.jaac.2013.05.006
  • Moon, J. & Ke, F. (2021). Exploring the treatment integrity of virtual reality-based social skills training for children with high functioning autism. Interactive Learning Environments, 29(6), 939–953. https://doi.org/10.1080/10494820.2019.1613665
  • Moore, C., Carter, R., Nietert, P., & Stewart P. (2011). Recommendations for Planning Pilot Studies in Clinical and Translational Research. Clin Transl Sci, 4 (5), 332- 3377. Doi: https://doi.org/10.1111/j.1752- 8062.2011.00347.x
  • Morales-Hidalgo, P., Voltas, N., Canals, J. (2021). Autism spectrum disorder prevalence and associated sociodemographic factors in the school population: EPINED study. Autism, 25(7), 1999-2011. https://doi.org/10.1177/13623613211007717
  • Muse, A., & Baldwin, J. (2021). Quasi-experimental research design. In J. Barnes (eds.), D. Forde (eds). The Encyclopedia of Research Methods in Criminology and Criminal Justice (pp.307-310). John Wiley & Sons: USA.
  • Mustafa, M. (2023). Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions. Computation, 11(3),1-23 https://doi.org/10.3390/computation11030052
  • Nerea, J., Negro, V. (2022). Review of the application of Artificial Neural Networks in ocean Engineering. Ocean Engineering,259(1), 1-13.https://doi.org/10.1016/j.oceaneng.2022.111947
  • Organización Mundial de la Salud. (2023). Autismo. Ginebra: Organización Mundial de la Salud. https://www.who.int/es/news-room/fact-sheets/detail/autism-spectrum-disorder
  • O’Keeffe, C., McNally, S. (2023). A Systematic Review of Play-Based Interventions Targeting the Social Communication Skills of Children with Autism Spectrum Disorder in Educational Contexts. Rev J Autism Dev Disord 10, (1) 51–81 https://doi.org/10.1007/s40489-021-00286-3
  • Palestra, G., Varni, G., Chetouani, M. and Esposito, F. (2016). A multimodal and multilevel system for robotics treatment of autism in children. En DAA '16: Proceedings of the International Workshop on Social Learning and Multimodal Interaction for Designing Artificial Agents (pp. 1-6). ACM: Digital Library: USA
  • Papoutsi C, Drigas A, Skianis C.(2018). Mobile applications to improve emotional intelligence in autism-a review. Int J Interact Mob Technol. 12(6), 47–61. doi: 10.3991/ijim.v12i6.9073
  • Parsons, S. (2016). Authenticity in virtual reality for assessment and intervention in autism: A conceptual review. Educational Research Review, 19, 138– 157. https://doi.org/10.1016/j.edurev.2016.08.001.
  • Paterson, M. (2023). Social robots and the futures of affective Touch. The sense and society, 18(2), 110-125. https://doi.org/10.1080/17458927.2023.2179231
  • Pérez-Aguiar, W. (1999). El estudio de casos. En F. J., Sarabia (Ed), Metodología para la investigación en marketing y dirección de empresas (pp. 108-110). Pirámide: España.
  • Perzolli, S., Bentenuto, A., Bertamini, G. & Venuti, P. (2023). Play with Me: How Fathers and Mothers Play with Their Preschoolers with Autism. Brain Sciences, 13(1),1-12. https://doi.org/10.3390/brainsci13010120
  • Pichon, S., De Gelder, B. & Grezes, J. (2009). Two different faces of threat. Comparing the neutral system for recognizing fear and anger in dynamic body expressions. Neuroimage, 47(4), 1873-1883. https://doi.org/10.1016/j.neuroimage.2009.03.084
  • Pour, A., Taheri, A., Alemi, M. & Meghdari, A. (2018). Human-Robot Facial Expression reciprocal interaction platform: case estudies on children with Autism. International Journal of Social Robotics, 10(1), 179-198. https://doi.org/10.1007/s12369-017-0461-4
  • Qiao, J., Li, F., Han, H. & Li, W. (2016). Constructive algorithm for fully connected cascade feedforward neural networks. Neurocomputing, 182 (1), 154-164. 10.1016/j.neucom.2015.12.003
  • Ramírez-Duque, A., Frizera-Neto, A. & Bastos, T.F. (2019). Robot-Assisted Autism Spectrum Disorder Diagnostic Based on Artificial Reasoning. Journal of intelligent and robotic systems, 96(1), 267-281. https://doi.org/10.1007/s10846-018-00975-y
  • Remington, A., Hanley, M., O’Brien, S., Riby, D. & Swettenham, J. (2019). Implications of capacity in the classroom: Simplifying tasks for autistic children may not be the answer. Research in Developmental Disabilities, 85(1), 197-204. https://doi.org/10.1016/j.ridd.2018.12.006
  • Robaczewski, A., Bouchard, J., Bouchard, K. & Gaboury, S. (2021). Socially assistive robots: The Specific case of the NAO. International Journal of Social Robotics, 13(1), 795-831. https://doi.org/10.1007/s12369-020-00664-7
  • Robins, B., Dautenhahn, K., Boekhorst, R. & Billard, A. (2005). Robotic assistants in therapy and education of children with autism. Can a small humanoid robot help encourage social interaction skills? Universal Access in the information Society, 4(1), 105-120. https://doi.org/10.1007/s10209-005-0116-3
  • Robins, B., Dautenhahn, K. and Dickerson, P. (2012). Embodiment and Cognitive Learning – Can a Humanoid Robot Help Children with Autism to Learn about Tactile Social Behaviour? En International Conference on Social Robotics (pp. 66-75). Springer: Alemania.
  • Rutter, M., Le Couter, A. & Lord, C. (2009). Autism Diagnostic Interview Revised. Autism Genetic Resource Exchange: Los Angeles (USA)
  • Salehi, H. & Burgueño, R. (2018). Emerging artificial intelligence methods in structural engineering. Elsevier, Amsterdam
  • Sartorato, F., Przybylowski, L. & Sarko, D. (2017). Improving therapeutic outcomes in autism spectrum disorders: Enhancing social communication and sensory processing through the use of interactive robots. Journal of Psychiatric Research, 90(1), 1-11. https://doi.org/10.1016/j.jpsychires.2017.02.004
  • Scheef, M., Pinto, J., Rahardja, K., Snibbe, S. & Tow, R. (2002). Experiences with Sparky, a social robot. In Socially Intelligent Agents, (pp. 173-180). Springer: USA.
  • Singh, A., Raj, K., Kumar, T., Verma, S. & Roy, A. (2023). Deep Learning-Based Cost-Effective and Responsive Robot for Autism Treatment. Drones, 7(2), 81-99. https://doi.org/10.3390/drones7020081
  • Stone, W., Ousley, O. and Littleford, C. (1997). Motor Imitation in Young Children with Autism: What's the Object? . J Abnorm Child Psychol 25, (1), 475–485. https://doi.org/10.1023/A:1022685731726
  • Snow, M., Hertzig, M. and Shapiro, T. (1987). Expression of emotion in young autistic children. Journal of the American Academy of Child and Adolescent Psychiatry, 26(1), 836-838,
  • So, W., Cheng, C., Lam, W. Huang, Y., Ng, K., Tung, H. & Wong, W. (2020). A Robot-Based Play-Drama Intervention May Improve the Joint Attention and Functional Play Behaviors of Chinese-Speaking Preschoolers with Autism Spectrum Disorder: A Pilot Study. Journal of Autism and Developmental Disorders, 50(1), 467-481. https://doi.org/10.1007/s10803-019-04270-z
  • Soori, M., Arezoo, B. & Dastres, R.(2023). Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive robotics, 3(1), 54-70. https://doi.org/10.1016/j.cogr.2023.04.001
  • Sorrentino, A., Fiorini, L., Mancioppi, G., Cavallo, F., Umbrico, A., Cesta, A., Orlandini, A. (2022). Personalizing care through robotic assistance and clinical supervision. Front. Robot. AI 9(1), 1-12. https://doi.org/10.3389/frobt.2022.883814
  • Stockemer, D. (2019). Quantitative Methods for the Social Sciences A Practical Introduction with Examples in SPSS and Stata. Springer: Canada.
  • Stone, W., Ousley, O., & Littleford, C. (1997). Motor imitation in young children with autism: What's the object? Journal of Abnormal Child Psychology, 25, 475–485. https://doi.org/10.1023/A:1022685731726
  • Syriopoulou-Delli, C. K. and Gkiolnta, E. 2022. Review of assistive technology in the training of children with autism spectrum disorders. International Journal of Developmental Disabilities, 68, 73–85. https://doi.org/10.1080/20473869.2019.1706333
  • Taheri, A., Meghdari, A., Alemi, M. & Pouretemad, H. (2018). Human-robot interaction in Autism Treaatment: A case study on three pairs of Autistic children as Twins, siblings, and Classmates. International Journal of Social Robotics, 10 (1), 93-113. https://doi.org/10.1007/s12369-017-0433-8
  • Taigman, Y., Yang, M., Ranzato, M. and Wolf, L. (2014). DeepFace: Closing the gap to human-level performance in face verification. En 2014 IEEE Conference on Computer vision and Pattern Recognition (pp.1701-1708). IEEE: USA.
  • Talaat, F. (2023). Real-time facial emotion recognition system among children with autism based on deep learning and IoT. Neural Comput & Applic 35, 12717–12728 https://doi.org/10.1007/s00521-023-08372-9
  • Tapeh, A. & Naser, M. (2023). Artificial Intelligence, Machine Learning, and Deep Learning in Structural Engineering: A Scientometrics Review of Trends and Best Practices. Arch Computat Methods Eng, 30 (1), 115–159 https://doi.org/10.1007/s11831-022-09793-w
  • Teslyuk, V., Kazarian, A., Kryvinska, N. & Tsmots, I. (2021). Optimal artificial neural network type selection method for usage in smart house systems. Sensors, 21(2), 1-14.https://doi.org/10.3390/s21010047
  • Tickle-Degnen, L. (2013). Nuts and Bolts of Conducting Feasibility Studies. The American Journal of Occupational Therapy, 67(2), 171-176. https://doi.org/10.5014/ajot.2013.006270
  • Tschida, J. and Yerys, B. (2021). A Systematic Review of the Positive Valence System in Autism Spectrum Disorder. Neuropsychol Rev, 31 (1), 58–88 https://doi.org/10.1007/s11065-020-09459-z
  • Trevisan, D. A., Hoskyn, M., & Birmingham, E. (2018). Facial expression production in autism: A meta‐analysis. Autism Research, 11(12), 1586–1601. https://doi.org/10.1002/aur.2037
  • Turan, B., Algedik, P., Yildirim, E., Gulsen, M., Cubukcu, H., Guler, M., Alarslan, H., Egemen, A. & Burak, O. (2023). Toward the detection of reduced emotion expression intensity: an autism sibling study. Journal of Clinical and Experimental Neuropsychology,45(3), 219-229. https://doi.org/10.1080/13803395.2023.2225234
  • Tschida, J. & Yerys, B. (2021). A Systematic Review of the Positive Valence System in Autism Spectrum Disorder. Neuropsychol Rev 31, 58–88 https://doi.org/10.1007/s11065-020-09459-z
  • Ullah Z, Al-Turjman F, Mostarda L, Gagliardi R (2020) Applications of artificial intelligence and machine learning in smart cities. Comput Commun, 154(15), 313-323. https://doi.org/10.1016/j.comcom.2020.02.069
  • Van Seters, J., Ossevoort, M., Tramper, J., Goedhart, M. (2012). The influence of student characteristics on the use of adaptative e-learning material. Computers and Education, 58(1), 942-952. 10.1016/j.compedu.2011.11.002
  • Verdú, E., Regueras, L. M., Gal, E., et al. (2017). Integration of an intelligent tutoring system in a course of computer network design. Educational Technology Research and Development, 65, 653–677. https://doi.org/10.1007/s11423-016-9503-0
  • Vrontis, D., Cristofi, M., Pereira, V., Tarba, S. & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies, and human resource management: a systematic review. The International Journal of Human Resource Management, 33(6), 1237-1266. https://doi.org/10.1080/09585192.2020.1871398
  • Waizbard-Bartov, E., Fein, D., Lord, C. & Amaral, D. (2023). Autism severity and its relationship to disability. Autism Research, 16 (4), 685-696. https://doi.org/10.1002/aur.2898
  • Walęcka, M., Wojciechowska, K. & Wichniak, A. (2022). Central coherence in adults with a high-functioning autism spectrum disorder. In a search for a non-self-reporting screening tool. Applied Neuropsychology: Adult, 29(4), 677-683. https://doi.org/10.1080/23279095.2020.1804908
  • Wilson, C., Brereton, M., Ploderer, B., Sitbon, L., & Saggers, B. (2017). Digital strategies for supporting strengths- and interests-based learning with children with autism. In Proceedings of the 19th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS'17), ACM, New York, NY, USA (pp. 52–61). https://doi.org/10.1145/3132525.3132553
  • Wijayasinghe, I., Ranatunga, I., Balakrishnan, N., Bugnariu, N. and Popa, O (2016). Human robot gesture analysis for objective assessment of autism spectrum disoder. International Journal of Social Robotics, 8(5), 695-707. DOI:10.1007/s12369-016-0379-2
  • Yin, R. (2002). Case study research: Design and methods. Thousand Oaks, CA: Sage.
  • Yeung, M. (2022). A systematic review and meta-analysis of facial emotion recognition in autism spectrum disorder: The specificity of deficits and the role of task characteristics. Neuroscience & Biobehavioral Reviews,133(1), 1-14.https://doi.org/10.1016/j.neubiorev.2021.104518
  • Zapata-Ros, M. (2018). The smart university. The transition from Learning Management Systems (LMS) to Smart Learning Systems (SLS) in Higher Education. Revista de Educación a Distancia, 57(10), 1-43. http://dx.doi.org/10.6018/red/57/10
  • Zhanatkyzy, A., Telisheva, Z., Amirova, A., Rakhymbayeva, N. and Sandygulova, A. (2023). Multi-Purposeful activities for Robot-Assisted Autism therapy: What works best for children’s social outcomes? In HRI '23: Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction (pp. 34-43). ACM Digital Library: USA.