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Educação e Ensino
Publicado: 2024-04-30

Strengthening AI via ToM and MC dimensions

Net Media Lab Mind - Brain R&D ΙΙΤ - N.C.S.R. "Demokritos", Athens, Greece
N.C.S.R. Demokritos
artificial intelligence, theory of mind, metacognition, computational theory of mind, autonomous systems


Theory of Mind (ToM) highlights the social-cognitive ability of the individual to communicate and interact effectively with the members of each social group. Essentially, it is the cornerstone of social knowledge that allows the recognition and understanding of the thoughts, intentions, and feelings of all involved, promoting social interaction and engagement. Metacognition (MC) is a higher mental ability of the biological mind and is characterized by the observation, control, evaluation, differentiation, and readjustment of the cognitive mechanism, aiming at its optimal performance and maintaining the homeostasis of mental, social, and emotional becoming of an organism. The rapid development of technology in recent decades has promoted the development of Artificial Intelligence (AI) intertwined with the need to integrate ToM and MC capabilities, enriching human communication. This paper investigates how the above-described human cognitive functions are involved in the conception and development of an artificial agent and their influence on human society. The conclusions suggest the importance of being able to read beliefs, emotions, and other factors, but also introspection by an intelligent system for social benefit, including the necessary ethical constraints.


  1. Anagnostopoulou, P., Alexandropoulou, V., Lorentzou, G., Lykothanasi, A., Ntaountaki, P., & Drigas, A. (2020). Artificial intelligence in autism assessment. International Journal of Emerging Technologies in Learning (iJET), 15(6), 95-107.
  2. Anderson, M. L., Fults, S., Josyula, D. P., Oates, T., Perlis, D., Wilson, S., & Wright, D. (2008). A Self-Help Guide For Autonomous Systems. AI Magazine, 29(2), 67.
  3. Aru, J., Labash, A., Corcoll, O., & Vicente, R. (2023). Mind the gap: challenges of deep learning approaches to Theory of Mind. Artificial Intelligence Review, 1-16.
  4. Bakola, L. N., Drigas, A., & Skianis, C. (2022). Emotional Intelligence vs. Artificial Intelligence: The interaction of human intelligence in evolutionary robotics. Research, Society and Development, 11(16).
  5. Bamicha, V., & Drigas, A. (2022a). The Evolutionary Course of Theory of Mind-Factors That Facilitate or Inhibit Its Operation & the Role of ICTs. Technium Soc. Sci. J., 30, 138-158.
  6. Bamicha, V., & Drigas, A. (2022b). ToM & ASD: The interconnection of Theory of Mind with the social-emotional, cognitive development of children with Autism Spectrum Disorder. The use of ICTs as an alternative form of intervention in ASD. Technium Social Sciences Journal, 33, 42-72.
  7. Bamicha, V., & Drigas, A. (2023a). Consciousness influences in ToM and Metacognition functioning-An artificial intelligence perspective. Res earch, Society and Development, 12(3).
  8. Bamicha, V., & Drigas, A. (2023b). Theory of Mind in relation to Metacognition and ICTs. A metacognitive approach to ToM. Scientific Electronic Archives, 16(4).
  9. Bamicha, V., & Salapata, Y. (2024). LLLT applications may enhance ASD aspects related to disturbances in the gut microbiome, mitochondrial activity, and neural network function. Brazilian Journal of Science, 3(1), 140-158.
  10. Brock, L. L., Kim, H., Gutshall, C. C., & Grissmer, D. W. (2018). The development of theory of mind: Predictors and moderators of improvement in kindergarten. Early Child Development and Care.
  11. Calì, C. (2020). Representation, Internal. In: Vercellone, F., Tedesco, S. (eds) Glossary of Morphology. Lecture Notes in Morphogenesis. Springer, Cham.
  12. Caro Piñeres, M. F., & Jiménez Builes, J. A. (2013). Analysis of models and metacognitive architectures in intelligent systems. Dyna, 80(180), 50-59. Retrieved from]
  13. Caro, M. F., Josyula, D. P., Cox, M. T., & Jiménez, J. A. (2014). Design and validation of a metamodel for metacognition support in artificial intelligent systems. Biologically Inspired Cognitive Architectures, 9, 82-104.
  14. Caro, M. F., Josyula, D. P., Jiménez, J. A., Kennedy, C. M., & Cox, M. T. (2015). A domain-specific visual language for modeling metacognition in intelligent systems. Biologically Inspired Cognitive Architectures, 13, 75-90.
  15. Caro, M. F., Gomez, A. A., & Giraldo, J. C. (2017). Algorithmic knowledge profiles for introspective monitoring in artificial cognitive agents. In 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC). doi: 10.1109/ICCI-CC.2017.8109792
  16. Çelikok, M. M., Peltola, T., Daee, P., & Kaski, S. (2019). Interactive AI with a Theory of Mind. arXiv preprint arXiv:1912.05284.
  17. Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial Intelligence trends in education: a narrative overview. Procedia Computer Science, 136, 16-24.
  18. Cheong, J. H., Jolly, E., Sul, S., & Chang, L. J. (2017). Computational models in social neuroscience. Computational models of brain and behavior, 229-244.
  19. Chaidi, I., & Drigas, A. (2022). Digital games & special education. Technium Soc. Sci. J., 34, 214.
  20. Chaidi, I., & Drigas, A. (2023). Digital Gaming and Autistic Spectrum Disorder. International Journal of Emerging Technologies in Learning (iJET), 18(22), 4-23. . DOI:10.3991/ijet.v18i22.34497
  21. Collins, J. A., & Fauser, B. C. (2005). Balancing the strengths of systematic and narrative reviews. Human reproduction update, 11(2), 103-104.
  22. Cox, M. T. (2005). Metacognition in computation: A selected research review. Artificial intelligence, 169(2), 104-141.
  23. Cox, M. T., & Raja, A. (2011). Metareasoning: An Introduction. Proc. Metareasoning, pp. 3-14.
  24. Cox, M., Mohammad, Z., Kondrakunta, S., Gogineni, V. R., Dannenhauer, D., & Larue, O. (2022). Computational metacognition. arXiv preprint arXiv:2201.12885.
  25. Crowder, J. A., & Shelli Friess MA, N. C. C. (2011). Metacognition and metamemory concepts for AI systems. In Proceedings on the International Conference on Artificial Intelligence (ICAI) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp). Retrieved from
  26. Cuzzolin, F., Morelli, A., Cirstea, B., & Sahakian, B. J. (2020). Knowing me, knowing you: theory of mind in AI. Psychological medicine, 50(7), 1057-1061.
  27. Dehaene, S., Lau, H., Kouider, S. (2021). What Is Consciousness, and Could Machines Have It? In: von Braun, J., S. Archer, M., Reichberg, G.M., Sánchez Sorondo, M. (eds) Robotics, AI, and Humanity. Springer, Cham, 43-56.
  28. Drigas, A. S. & Papas, M. A. (2017). The Consciousness-Intelligence-Knowledge Pyramid: An 8x8 Layer Model. International Journal of Recent Contributions from Engineering Science & IT, 5(3), 14-25.
  29. Drigas, A. & Mitsea, E. (2020a). The Triangle of Spiritual Intelligence, Metacognition and Consciousness. International Journal of Recent Contributions from Engineering Science & IT, 8(1), 4-23.
  30. Drigas, A. & Mitsea, E. (2020b). A Metacognition Based 8 Pillars Mindfulness Model and Training Strategies. International Journal of Recent Contributions from Engineering Science & IT, 8(4), 4-17.
  31. Drigas, A. & Mitsea, E. (2020c). The 8 Pillars of Metacognition. International Journal of Recent Contributions from Engineering Science & IT, 15(21), 162-178.
  32. Drigas, A., Kokkalia, G. & Economou, A. (2021). An 8-Layer Model for Metacognitive Skills in Kindergarten. NEUROLOGY AND NEUROBIOLOGY, 4(1), 2-10.
  33. Erb, B. (2016). Artificial Intelligence & Theory of Mind. Ulm University (2016), 1-11. Retrieved from
  34. Ertel, W. (2011). Introduction. In: Introduction to Artificial Intelligence. Undergraduate Topics in Computer Science. Springer, London.
  35. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American psychologist, 34(10), 906.
  36. Frith, U. & Happé, F.G.E (1999). Theory of Mind and Self-Consciousness: What Is It Like to Be Autistic? Mind & Language, 14 (1), 1–22.
  37. Fotoglou, A., Moraiti, I., Dona, K., Katsimperi, A., Tsionakas, K., Karabatzaki, Z., & Drigas, A. (2022). IoT Applications help people with Autism. Technium Soc. Sci. J., 31, 115. DOI: 10.47577/tssj.v31i1.6422
  38. García, C. G., Valdez, E. R. N., Díaz, V. G., Bustelo, B. C. P. G., & Lovelle, J. M. C. (2019). A Review of Artificial Intelligence in the Internet of Things. IJIMAI, 5(4), 9-20. DOI: 10.9781/ijimai.2018.03.004
  39. Garcia-Lopez, A. (2024). Theory of Mind in Artificial Intelligence Applications. In The Theory of Mind Under Scrutiny: Psychopathology, Neuroscience, Philosophy of Mind and Artificial Intelligence (pp. 723-750). Cham: Springer Nature Switzerland.
  40. Garg, S., & Sharma, S. (2020). Impact of artificial intelligence in special need education to promote inclusive pedagogy. International Journal of Information and Education Technology, 10(7), 523-527. doi: 10.18178/ijiet.2020.10.7.1418
  41. González, B., & Chang, L.J. (2021). Computational Models of Mentalizing. In: Gilead, M., Ochsner, K.N. (eds) The Neural Basis of Mentalizing. Springer, Cham.
  42. Jackson, P. (2020). Toward metascience via human-level AI with metacognition. Procedia Computer Science, 169, 527-534.
  43. Johnson, B. (2022). Metacognition for artificial intelligence system safety–An approach to safe and desired behavior. Safety Science, 151, 105743.
  44. Karyotaki, M., & Drigas, A. (2015). Online and other ICT Applications for Cognitive Training and Assessment. International Journal of Online Engineering, 11(2).
  45. Kralik, J. D., Lee, J. H., Rosenbloom, P. S., Jackson Jr, P. C., Epstein, S. L., Romero, O. J., ... & McGreggor, K. (2018). Metacognition for a common model of cognition. Procedia computer science, 145, 730-739.
  46. Kuchling, F., Fields, C., & Levin, M. (2022). Metacognition as a Consequence of Competing Evolutionary Time Scales. Entropy (Basel, Switzerland), 24(5), 601.
  47. Kyriakaki, E., Karabatzaki, Z., & Salapata, Y. (2023). Mobile applications for autism. Eximia, 8, 51-66. Retrieved from
  48. Langdon, A., Botvinick, M., Nakahara, H., Tanaka, K., Matsumoto, M., & Kanai, R. (2022). Meta-learning, social cognition and consciousness in brains and machines. Neural Networks, 145, 80-89.
  49. Langley, C., Cirstea, B. I., Cuzzolin, F., & Sahakian, B. J. (2022). Theory of mind and preference learning at the interface of cognitive science, neuroscience, and AI: A review. Frontiers in Artificial Intelligence, 5, 62.
  50. Langlois, S. T., Akoroda, O., Carrillo, E., Herrmann, J. W., Azarm, S., Xu, H., & Otte, M. (2020). Metareasoning structures, problems, and modes for multiagent systems: A survey. IEEE Access, 8, 183080-183089.
  51. Liu, J., Kong, X., Xia, F., Bai, X., Wang, L., Qing, Q., & Lee, I. (2018). Artificial Intelligence in the 21st Century. IEEE Access, 6, 34403-34421.
  52. Macpherson, T., Churchland, A., Sejnowski, T., DiCarlo, J., Kamitani, Y., Takahashi, H., & Hikida, T. (2021). Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research. Neural Networks.
  53. M’Balé, K., & Josyula, D. (2013). Integrating Metacognition into Artificial Agents. Common Model of Cognition Bulletin, 1(1), 55–62. Retrieved from
  54. McCarthy, J. (2007). What is artificial intelligence?
  55. Moraiti, I., & Drigas, A. (2023). AI Tools Like ChatGPT for People with Neurodevelopmental Disorders. International Journal of Online & Biomedical Engineering, 19(16). DOI:10.3991/ijoe.v19i16.43399
  56. Moraiti, I., Fotoglou, A., & Drigas, A. (2023). Digital and Mobile Applications for Autism Inclusion. International Journal of Online & Biomedical Engineering, 19(11).
  57. Nebreda, A., Shpakivska-Bilan, D., Camara, C., & Susi, G. (2024). The Social Machine: Artificial Intelligence (AI) Approaches to Theory of Mind. In The Theory of Mind Under Scrutiny: Psychopathology, Neuroscience, Philosophy of Mind and Artificial Intelligence (pp. 681-722). Cham: Springer Nature Switzerland.
  58. Nguyen, T. N., & Gonzalez, C. (2020). Cognitive Machine Theory of Mind. In CogSci, 2560-2566
  59. Papanastasiou, G., Drigas, A., & Skianis, C. (2022). Serious Games: How do they impact special education needs children. Technium Education and Humanities, 2(3), 41-58.
  60. Pappas, M. A., & Drigas, A. S. (2016). Incorporation of Artificial Intelligence Tutoring Techniques in Mathematics. International Journal of Engineering Pedagogy, 6(4),12-16.
  61. Pitt, D. (2022). Mental Representation. The Stanford Encyclopedia of Philosophy, Edward N. Zalta & Uri Nodelman (eds.), URL:
  62. Rabinowitz, N., Perbet, F., Song, F., Zhang, C., Eslami, S. A., & Botvinick, M. (2018, July). Machine theory of mind. In International conference on machine learning (pp. 4218-4227). PMLR.
  63. Rakoczy, H. (2022). Foundations of theory of mind and its development in early childhood. Nature Reviews Psychology, 1(4), 223-235.
  64. Rescorla, M. (2015). The Computational Theory of Mind. The Stanford Encyclopedia of Philosoph, Edward N. Zalta (ed.), URL:
  65. Ribeiro, B.A., Coelho, H., Ferreira, A.E., Branquinho, J. (2024). Metacognition, Accountability and Legal Personhood of AI. In: Sousa Antunes, H., Freitas, P.M., Oliveira, A.L., Martins Pereira, C., Vaz de Sequeira, E., Barreto Xavier, L. (eds) Multidisciplinary Perspectives on Artificial Intelligence and the Law. Law, Governance and Technology Series, vol 58. Springer, Cham.
  66. Roth, M., Marsella, S., & Barsalou, L. (2022). Cutting Corners in Theory of Mind. Retrieved from:
  67. Sadighi, A., Donyanavard, B., Kadeed, T., Moazzemi, K., Muck, T., Nassar, A., … Kurdahi, F. (2018). Design methodologies for enabling self-awareness in autonomous systems. In 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE).
  68. Samoili, S., López Cobo, M., Gómez, E., De Prato, G., Martínez-Plumed, F., & Delipetrev, B. (2020). AI Watch. Defining Artificial Intelligence. Towards an operational definition and taxonomy of artificial intelligence. EUR 30117 EN, Publications Office of the European Union, Luxembourg, ISBN 978-92-76-17045-7. doi:10.2760/382730, JRC118163
  69. Samson, D. (2009). Reading other people's mind: Insights from neuropsychology. Journal of Neuropsychology, 3(1), 3-16.
  70. Schmill, M. D., Oates, T., Anderson, M. L., Josyula, D., Perlis, D., Wilson, S., & Fults, S. (2008). The role of metacognition in robust AI systems. 163-170. Paper presented at 2008 AAAI Workshop, Chicago, IL, United States. Retrieved from:
  71. Schossau, J., & Hintze, A. (2023). Towards a Theory of Mind for Artificial Intelligence Agents. In ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference. MIT Press.
  72. Seshia, S.A. (2019). Introspective Environment Modeling. In: Finkbeiner, B., Mariani, L. (eds) Runtime Verification. RV 2019. Lecture Notes in Computer Science(), vol 11757. Springer, Cham.
  73. Sideraki, A., & Drigas, A. (2021). Artificial Intelligence (AI) in Autism. Technium Soc. Sci. J., 26, 262.
  74. Snyder, H. (2019). "Literature review as a research methodology: An overview and guidelines," Journal of Business Research, Elsevier, 104(C), 333-339.
  75. Sodian, B., & Frith, U. (2008). Metacognition, theory of mind, and self‐control: The relevance of high‐level cognitive processes in development, neuroscience, and education. Mind, Brain, and Education, 2(3), 111–113.
  76. Swiatczak, B. (2011). Conscious representations: An intractable problem for the computational theory of mind. Minds and Machines, 21(1), 19-32.
  77. Tourimpampa, A., Drigas, A., Economou, A., & Roussos, P. (2018). Perception and text comprehension. It’s a matter of perception! International Journal of Emerging Technologies in Learning (Online), 13(7), 228.
  78. Tyagi, N. (2021). 6 Major branches of artificial intelligence (AI). Artificial Intelligence, analyticSteps. Retrieved from
  79. Vrettaros, J., Vouros, G., Drigas, A. (2006). An Intelligent System for Solo Taxonomy. In: Shi, Z., Shimohara, K., Feng, D. (eds) Intelligent Information Processing III. IIP 2006. IFIP International Federation for Information Processing, vol 228. Springer, Boston, MA.
  80. Williams, J., Fiore, S. M., & Jentsch, F. (2022). Supporting Artificial Social Intelligence With Theory of Mind. Frontiers in artificial intelligence, 5, 750763.
  81. Worley, P. (2018). Plato, metacognition and philosophy in schools. Journal of Philosophy in Schools, 5(1).
  82. Yamato, Y., Suzuki, R., & Arita, T. (2020). Evolution of metamemory based on self-reference in artificial neural network with neuromodulation. Criterion, 1, C1. Retrieved from
  83. Yang, G. Z., Bellingham, J., Dupont, P. E., Fischer, P., Floridi, L., Full, R., ... & Wood, R. (2018). The grand challenges of science robotics. Science robotics, 3(14), eaar7650.
  84. Zaroukian, E. (2022). Theory of Mind and Metareasoning for Artificial Intelligence: A Review. Retrieved from
  85. Zhao, J., Wu, M., Zhou, L., Wang, X., & Jia, J. (2022). Cognitive psychology-based artificial intelligence review. Frontiers in Neuroscience, 16.

Como Citar

Bamicha , V. ., & Drigas, A. (2024). Strengthening AI via ToM and MC dimensions. Scientific Electronic Archives, 17(3).