Connected Intelligence: Conceptual Foundations and Evolution Beyond Artificial Intelligence

Authors

  • KA. Vanessa Research Scholar, Department of Rural Industries and Management, The Gandhigram Rural Institute-Deemed to be University, Gandhigram, Dindigul District, Tamil Nadu, India Author https://orcid.org/0009-0009-3789-7492
  • H. Samuel Thavaraj Associate Professor, Department of Rural Industries and Management, The Gandhigram Rural Institute-Deemed to be University, Gandhigram, Dindigul District, Tamil Nadu, India Author https://orcid.org/0009-0005-7541-4608
  • V. Monica Hepzibah Pushpabai Associate Professor & Head, Department of Journalism & Mass Communication, Rani Anna Government College for Women, Tirunelveli, Tamil Nadu, India Author https://orcid.org/0009-0007-5493-8378
  • N. Robindro Singh Assistant Professor, College of Business and Economics, University of Kabridahar, Ethiopia, Author https://orcid.org/0009-0009-7429-3663

DOI:

https://doi.org/10.63503/aisem.48

Keywords:

Connected Intelligence, Artificial Intelligence, Emotional Intelligence, Human-AI Collaboration, Human-Centric Systems

Abstract

This paper discusses the up-and-coming notion of Connected Intelligence as the development of the 
traditional Artificial Intelligence. As Artificial Intelligence deals with decision-making based on machines, Connected 
Intelligence improves on the interrelationship between human, technological, and organizational capacity and the interaction with interrelated systems. The paper analyzes the underlying conceptual basis and the development of human intelligence to artificial and collective intelligence, which finally approaches Connected Intelligence. The effectiveness of interrelated systems in terms of empathy and ethical decision-making and improved human-AI interaction are the key topics of the Emotional Intelligence role in this review. By synthesizing the existing literature, the research identifies important aspects, applications and concerns relating to Connected Intelligence. Moreover, it also highlights major research gaps, especially the embarrassing research conducted on the incorporation of Emotional Intelligence into intelligent ecosystems. The paper concludes with a proposal for a human-centric framework and future research directions for developing intelligent systems that are sustainable and can understand people.  

References

[1] Y. K. Dwivedi et al., “International Journal of Information Management Opinion Paper : ‘ So what if ChatGPT wrote it ? ’ Multidisciplinary perspectives on opportunities , challenges and implications of generative conversational AI for research , practice and policy ☆,” vol. 71, no. March, 2023, doi: 10.1016/j.ijinfomgt.2023.102642.

[2] L. Floridi, “AI as Agency Without Intelligence : on ChatGPT , Large Language Models , and Other Generative Models,” pp. 1–7, 2023, doi: 10.1007/s13347-023-00621-y.

[3] A. Sharma, I. W. Lin, A. S. Miner, D. C. Atkins, and T. Althoff, “Human – AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support,” vol. 5, no. January, 2023, doi: 10.1038/s42256-022-00593-2.

[4] A. Rai, “Explainable AI: from black box to glass box,” pp. 137–141, 2020.

[5] X. Kong, H. Fang, W. Chen, J. Xiao, and M. Zhang, “Examining human–AI collaboration in hybrid intelligence learning environments: insight from the Synergy Degree Model,” Humanit. Soc. Sci. Commun., no. 2025, pp. 1–14, doi: 10.1057/s41599-025-05097-z.

[6] M. G. Jacobides, S. Brusoni, and F. Candelon, “The Evolutionary Dynamics of the Artificial Intelligence Ecosystem The Evolutionary Dynamics of the Artificial Intelligence Ecosystem,” no. April 2026, 2021.

[7] “Emotional Intelligence in Artificial Intelligence, A review and evaluation study,” 2024.

[8] H. Zhang, Y. Liu, M. Jiang, J. Chen, M. Wang, and F. Paas, “Emotional Artificial Intelligence in Education: A Systematic Review and Meta-Analysis,” Educ. Psychol. Rev., vol. 37, no. 4, 2025, doi: 10.1007/s10648

025-10086-4.

[9] A. Deroncele-acosta, J. Luis, M. Ayala, and A. Barrasa, “Integrating artificial intelligence to assess emotions in learning environments : a systematic literature review,” no. June, 2024, doi: 10.3389/fpsyg.2024.1387089.

[10] R. J. Sternberg, “A theory of adaptive intelligence and its relation to general intelligence,” J. Intell., vol. 7, no. 4, pp. 1–17, 2019, doi: 10.3390/jintelligence7040023.

[11] A. Barredo Arrieta et al., “Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Inf. Fusion, vol. 58, pp. 82–115, 2020, doi: 10.1016/j.inffus.2019.12.012.

[12] F. Chollet, “On the Measure of Intelligence,” pp. 1–64, 2019, [Online]. Available: http://arxiv.org/abs/1911.01547

[13] Y. K. Dwivedi et al., “Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy,” Int. J. Inf. Manage., vol. 57, no. August 2019, p. 101994, 2021, doi: 10.1016/j.ijinfomgt.2019.08.002.

[14] K. Ofosu-Ampong, “Artificial intelligence research: A review on dominant themes, methods, frameworks and future research directions,” Telemat. Informatics Reports, vol. 14, no. March, p. 100127, 2024, doi: 10.1016/j.teler.2024.100127.

[15] P. Shrestha, B. Shrestha, J. Sherestha, and J. Chen, “Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis: A Literature Review,” J. Nepal Med. Assoc., vol. 63, no. 283, pp. 205–211, 2025, doi: 10.31729/jnma.8897.

[16] S. Makridakis, “The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms,” Futures, vol. 90, pp. 46–60, 2017, doi: 10.1016/j.futures.2017.03.006.

[17] M. Khaneghaei, D. Asadi, B. Ebrahimi, Ö. Tutsoy, and Y. Nabavi Chashmi, “Intelligent hybrid optimization algorithms for multi-agent aerial robots path planning: review of the recent emerging trends and open research directions,” Artif. Intell. Rev., 2026, doi: 10.1007/s10462-025-11472-8.

[18] A. Jobin, M. Ienca, and E. Vayena, “The global landscape of AI ethics guidelines,” Nat. Mach. Intell., vol. 1, no. 9, pp. 389–399, 2019, doi: 10.1038/s42256-019-0088-2.

[19] N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, “A Survey on Bias and Fairness in Machine Learning,” ACM Comput. Surv., vol. 54, no. 6, 2022, doi: 10.1145/3457607.

[20] M. H. Jarrahi, “Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making,” Bus. Horiz., vol. 61, no. 4, pp. 577–586, 2018, doi: 10.1016/j.bushor.2018.03.007.

[21] S. Raisch and S. Krakowski, “Artificial Intelligence and Management: The Automation-Augmentation Paradox,” Acad. Manag. Rev., vol. 46, no. 1, pp. 192–210, 2020, doi: 10.5465/2018.0072.

[22] M. Fiori, S. Agnoli, and S. K. Davis, “Editorial: New trends in emotional intelligence: conceptualization, understanding, and assessment,” Front. Psychol., vol. 14, 2023, doi: 10.3389/fpsyg.2023.1266076. [23] L. M. Bru-Luna, M. Martí-Vilar, C. Merino-Soto, and J. L. Cervera-Santiago, “Emotional intelligence measures: A systematic review,” Healthc., vol. 9, no. 12, pp. 1–36, 2021, doi: 10.3390/healthcare9121696.

[24] N. Sánchez-Álvarez, M. P. Berrios Martos, and N. Extremera, “A Meta-Analysis of the Relationship Between Emotional Intelligence and Academic Performance in Secondary Education: A Multi-Stream Comparison,” Front. Psychol., vol. 11, no. July, pp. 1–11, 2020, doi: 10.3389/fpsyg.2020.01517.

[25] Susan Mattingly, “Teacher Perspectives on the Instruction of Workplace Transferable 21 St Century Skills in the High School Setting,” 2024.

[26] R. Yuvaraj, R. Mittal, A. A. Prince, and J. S. Huang, “Affective Computing for Learning in Education: A Systematic Review and Bibliometric Analysis,” Educ. Sci., vol. 15, no. 1, 2025, doi:

10.3390/educsci15010065.

[27] I. Kotsou, M. Mikolajczak, A. Heeren, J. Grégoire, and C. Leys, “Improving Emotional Intelligence: A Systematic Review of Existing Work and Future Challenges,” Emot. Rev., vol. 11, no. 2, pp. 151–165, 2019,

doi: 10.1177/1754073917735902.

[28] N. Extremera, “Pathways between Ability Emotional Intelligence and Subjective Well-Being : Bridging Links through Cognitive Emotion Regulation Strategies,” 2020.

[29] D. M. Resurrección, Ó. Jiménez, E. Menor, and D. Ruiz-Aranda, “The Learning to Be Project: An Intervention for Spanish Students in Primary Education,” Front. Psychol., vol. 12, no. April, pp. 1–8, 2021, doi: 10.3389/fpsyg.2021.632617.

[30] Q. Peng and L. Shuhong, “Emotional factor matters in language learning? A meta-analysis of emotional intelligence on language achievement,” Front. Psychol., vol. 16, no. May, pp. 1–14, 2025, doi: 10.3389/fpsyg.2025.1502112.

[31] D. Dellermann, N. Lipusch, P. Ebel, and J. M. Leimeister, “Design principles for a hybrid intelligence decision support system for business model validation,” Electron. Mark., vol. 29, no. 3, pp. 423–441, 2019, doi: 10.1007/s12525-018-0309-2.

[32] J. Prakash, L. Murali, N. Manikandan, N. Nagaprasad, and K. Ramaswamy, “A vehicular network based intelligent transport system for smart cities using machine learning algorithms,” Sci. Rep., vol. 14, no. 1, 2024, doi: 10.1038/s41598-023-50906-7. Learning

[33] M. N. Rosendo, “Enhancing the Tri-Dimensional Awareness of an Autonomous Surface Vehicle through Machine Algorithms,” 2022, aberto.up.pt/bitstream/10216/140752/2/549939.pdf

[Online]. Available: https://repositorio-aberto.up.pt/bitstream/10216/140752/2/549939.pdf

[34] F. C. Ogenyi, C. N. Ugwu, and O. P. C. Ugwu, “A comprehensive review of AI-native 6G: integrating semantic communications, reconfigurable intelligent surfaces, and edge intelligence for next-generation connectivity,” Front. Commun. Networks, vol. 6, no. September, pp. 1–15, 2025, doi: 10.3389/frcmn.2025.1655410.

[35] H. Kuai, J. X. Huang, X. Tao, G. Pasi, and Y. Yao, “World To Create a Future Intelligent Society,” pp. 1 50, 2025.

[36] N. Sciences, “Hybrid Intelligence Systems Augmentation of Humans and AI,” 2022. Pacific

[37] P. Hemmer and M. Schemmer, “Human-AI Complementarity in Hybrid Intelligence Systems: A Structured Literature Review Needmining View project Artificial Intelligence in Service Systems View project,” Proc. 25th Asia Conf. Inf. Syst. 2021, https://www.researchgate.net/publication/352882174 pp. 1–14, 2021, [Online]. Available: https://www.researchgate.net/publication/352882174

[38] K. Arar, A. Tlili, L. Schunka, S. Salha, and A. Saiti, “Reimagining Educational Leadership and Management Through Artificial Intelligence: An Integrative Systematic Review,” Leadersh. Policy Sch., vol. 24, no. 1, pp. 4–26, 2025, doi: 10.1080/15700763.2025.2451982.

[39] S. F. Pileggi, “Ontology in Hybrid Intelligence: A Concise Literature Review,” Futur. Internet, vol. 16, no. 8, 2024, doi: 10.3390/fi16080268.

[40] F. S. Khan, S. S. Mazhar, K. Mazhar, D. A. AlSaleh, and A. Mazhar, “Model-agnostic explainable artificial intelligence methods in finance: a systematic review, recent developments, limitations, challenges and future directions,” Artif. Intell. Rev., vol. 58, no. 8, 2025, doi: 10.1007/s10462-025-11215-9.

[41] Y. Feng, M. Mohammadi, L. Wang, M. Rashidi, and P. Mehrabi, “Application of artificial intelligence to evaluate the fresh properties of self-consolidating concrete,” Materials (Basel)., vol. 14, no. 17, pp. 1–21, 2021, doi: 10.3390/ma14174885.

[42] R. Abebe, S. Barocas, J. Kleinberg, K. Levy, M. Raghavan, and D. G. Robinson, “Roles for computing in social change,” FAT* 2020 - Proc. 2020 Conf. Fairness, Accountability, Transpar., pp. 252–260, 2020, doi: 10.1145/3351095.3372871.

[43] D. Calvo, L. Cano-Orón, and J. Morales-i-Gras, “Unstoppable Implementation. Technological Imaginaries on Artificial Intelligence in Southern European Journalism,” Journal. Pract., vol. 19, no. 10, pp. 2209–2229, Oct. 2025, doi: 10.1080/17512786.2025.2522471.

[44] F. Ribeiro, F. Fidalgo, A. Silva, J. Metrôlho, O. Santos, and R. Dionisio, “Literature review of machine learning algorithms for pressure ulcer prevention: Challenges and opportunities,” Informatics, vol. 8, no. 4, 2021, doi: 10.3390/informatics8040076.

[45] S. A. Salloum, K. M. Alomari, A. M. Alfaisal, R. A. Aljanada, and A. Basiouni, “Emotion recognition for enhanced learning: using AI to detect students’ emotions and adjust teaching methods,” Smart Learn. Environ., vol. 12, no. 1, 2025, doi: 10.1186/s40561-025-00374-5.

[46] L. Floridi, “The End of an Era: from Self-Regulation to Hard Law for the Digital Industry,” Philos. Technol., vol. 34, no. 4, pp. 619–622, 2021, doi: 10.1007/s13347-021-00493-0.

[47] B. C. Stahl et al., “Artificial intelligence for human flourishing – Beyond principles for machine learning,” J. Bus. Res., vol. 124, no. December 2020, pp. 374–388, 2021, doi: 10.1016/j.jbusres.2020.11.030.

[48] N. Tahir and R. Parasuraman, “Edge Computing and Its Application in Robotics: A Survey,” J. Sens. Actuator Networks, vol. 14, no. 4, 2025, doi: 10.3390/jsan14040065.

[49] K. Siau, C. Woo, V. C. Storey, R. H. L. Chiang, C. E. H. Chua, and J. W. Beard, “Information Systems Analysis and Design: Past Revolutions, Present Challenges, and Future Research Directions,” Commun. Assoc. Inf. Syst., vol. 50, no. 1, pp. 835–856, 2022, doi: 10.17705/1CAIS.05037.

Published

2026-10-25