Self++: Co-determined agency for human-AI symbiosis in extended reality

Abstract

Self++ is a conceptual design framework for human-Artificial Intelligence (AI) symbiosis in extended reality (XR) that preserves human authorship while still benefiting from increasingly capable AI agents. Because XR can shape both perceptual evidence and action, apparently ‘helpful’ assistance can drift into over-reliance, covert persuasion, and blurred responsibility. Self++ grounds interaction in two complementary theories: Self-Determination Theory (autonomy, competence, relatedness) and the Free Energy Principle (predictive stability under uncertainty). It operationalises these foundations through co-determination, treating the human and the AI as a coupled system that must keep intent and limits legible, tune support over time, and preserve the user’s right to endorse, contest, and override. These requirements are summarised as the co-determination principles (T.A.N.): Transparency, Adaptivity, and Negotiability. Self++ organises augmentation into three concurrently activatable overlays spanning sensorimotor competence support (Self: competence overlay), deliberative autonomy support (Self+: autonomy overlay), and social and long-horizon relatedness and purpose support (Self++: relatedness and purpose overlay). Across the overlays, it specifies nine role patterns (Tutor, Skill Builder, Coach; Choice Architect, Advisor, Agentic Worker; Contextual Interpreter, Social Facilitator, Purpose Amplifier) that can be implemented as interaction patterns, not personas. The contribution is a role-based conceptual map that generates testable design propositions for XR-AI systems that grow capability without replacing judgement, enabling symbiotic agency in work, learning, and social life and resilient human development.

Keywords

Extended reality / human-AI symbiosis / human-AI teaming / self-determination theory / free energy principle / co-determination / augmented agency / interaction design / trustworthy AI

References

[1] Licklider JC. Man-computer symbiosis. IRE Trans Hum Factors Electron. 1960; 1: 4-11.

[2] Engelbart DC. Augmenting human intellect: A conceptual framework (1962). In: Ideas that created the future. Cambridge: The MIT Press; 2021. p. 225-236.

[3] Clark A, Chalmers D. The extended mind. Analysis. 1998; 58(1): 7-19.

[4] Hutchins E. Cognition in the wild. Cambridge: MIT Press. 1995.

[5] Sparrow B, Liu J, Wegner DM. Google effects on memory: Cognitive consequences of having information at our fingertips. Science. 2011; 333(6043): 776-778.

[6] Fisher M, Goddu MK, Keil FC. Searching for explanations: How the Internet inflates estimates of internal knowledge. J Exp Psychol Gen. 2015; 144(3): 674-687.

[7] Dahmani L, Bohbot VD. Habitual use of GPS negatively impacts spatial memory during self-guided navigation. Sci Rep. 2020; 10(1): 6310.

[8] Kirsh D. Thinking with external representations. AI Soc. 2010; 25(4): 441-454.

[9] Hollan J, Hutchins E, Kirsh D. Distributed cognition: Toward a new foundation for human-computer interaction research. ACM Trans Comput Hum Interact. 2000; 7(2): 174-196.

[10] Bostrom N. Superintelligence: Paths, dangers, strategies. New York: Oxford University Press; 2014.

[11] Bostrom N, Yudkowsky E. The ethics of artificial intelligence. In: Yampolskiy RV, editor. Artificial intelligence safety and security. New York: Chapman and Hall/CRC; 2018. p. 57-69.

[12] Lee HH, Sarkar A, Tankelevitch L, Drosos I. The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. In: Yamashita N, Evers V, Yatani K, Ding X, Lee B, Chetty M, Toups-Dugas P, editors. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems; 2025 Apr 26-May 1; Yokohama, Japan. New York: Association for Computing Machinery; 2025. p. 1-22.

[13] Pinker S. The cognitive niche: Coevolution of intelligence, sociality, and language. Proc Natl Acad Sci U S A. 2010; 107(supplement_2): 8993-8999.

[14] Clark A. Natural-born cyborgs: Minds, technologies, and the future of human intelligence. New York: Oxford University Press; 2003.

[15] Clark A. Précis of Supersizing the mind: Embodiment, action, and cognitive extension (Oxford University Press, NY, 2008). Philos Stud. 2011; 152(3): 413-416.

[16] Friston K. The free-energy principle: A unified brain theory? Nat Rev Neurosci. 2010; 11(2): 127-138.

[17] Thanyadit S, Punpongsanon P, Piumsomboon T, Pong TC. XR-LIVE: Enhancing asynchronous shared-space demonstrations with spatial-temporal assistive toolsets for effective learning in immersive virtual laboratories. Proc ACM Hum Comput Interact. 2022; 6(CSCW1): 1-23.

[18] Zhang J, Han B, Dong Z, Wen R. Virtual triplets: A mixed modal synchronous and asynchronous collaboration with human-agent interaction in virtual reality. In: Mueller FF, Kyburz P, Williamson JR, Sas C, editors. Extended Abstracts of the CHI Conference on Human Factors in Computing Systems; 2024 May 11-16; Honolulu, USA. New York: Association for Computing Machinery; 2024. p. 1-8.

[19] Zhang J, Han B, Dong Z, Topliss J, Yousefi M, Lee GA, et al. HAT swapping: Virtual agents as stand-ins for absent human instructors in virtual training. IEEE Trans Vis Comput Graph. 2025; 31(11): 9995-10004.

[20] Doudkin A, Pataranutaporn P, Maes P. From synthetic to human: The gap between AI-predicted and actual pro-environmental behavior change after chatbot persuasion. In: Sin J, Law E, Wallace J, Munteanu C, Korre D, editors. Proceedings of the 7th ACM Conference on Conversational User Interfaces; 2025 Jul 8-10; Waterloo, Canada. New York: Association for Computing Machinery; 2025. p. 1-18.

[21] Liu AR, Pataranutaporn P, Maes P. The heterogeneous effects of AI companionship: An empirical model of chatbot usage and loneliness and a typology of user archetypes. ACM Conf AI Ethics Soc. 2025; 8(2): 1585-1597.

[22] Klien G, Woods DD, Bradshaw JM, Hoffman RR, Feltovich PJ. Ten challenges for making automation a “team player” in joint human-agent activity. IEEE Intell Syst. 2004; 19(6): 91-95.

[23] Vaccaro M, Almaatouq A, Malone T. When combinations of humans and AI are useful: A systematic review and meta-analysis. Nat Hum Behav. 2024; 8(12): 2293-2303.

[24] Yousefi M, Shahi A, Sharifi M, J Jorge Romera A, Hoermann S, Piumsomboon T. Team dynamics in human-AI collaboration: Effects on confidence, satisfaction, and accountability. In: Subramanian R, Nakano YI, Gedeon T, Kankanhalli M, Guha T, Shukla J, Mohammadi G, Celiktutan O, editors. Proceedings of the 27th International Conference on Multimodal Interaction; 2025 Oct 13-17; Canberra, Australia. New York: Association for Computing Machinery; 2025. p. 398-404.

[25] Bansal G, Nushi B, Kamar E, Horvitz E, Weld DS. Is the most accurate AI the best teammate? Optimizing AI for teamwork. In: Proceedings of the AAAI Conference on Artificial Intelligence; 2021 Feb 2-9; Singapore. Washington: Association for the Advancement of Artificial Intelligence; 2021. p. 11405-11414.

[26] Mueller F, Semertzidis N, Andres J, Marshall J, Benford S, Li X, et al. Toward understanding the design of intertwined human-computer integrations. ACM Trans Comput-Hum Interact. 2023; 30(5): 1-45.

[27] Zhou F, Duh HB, Billinghurst M. Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR. In: Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality; 2008 Sep 15-18. Washington: IEEE Computer Society; 2008. p. 193-202.

[28] Kim K, Billinghurst M, Bruder G, Duh HB, Welch GF. Revisiting trends in augmented reality research: A review of the 2nd decade of ISMAR (2008-2017). IEEE Trans Vis Comput Graph. 2018; 24(11): 2947-2962.

[29] Norouzi N, Kim K, Bruder G, Bailenson JN, Wisniewski P, Welch GF. The advantages of virtual dogs over virtual people: Using augmented reality to provide social support in stressful situations. Int J Hum Comput Stud. 2022; 165: 102838.

[30] Pan X, Hamilton AFC. Why and how to use virtual reality to study human social interaction: The challenges of exploring a new research landscape. Br J Psychol. 2018; 109(3): 395-417.

[31] Wen R, Li Q, Pu W, Mu R, Nassani A, Hoermann S, et al. GenLinguaScape: Enabling user-defined VR scenarios for communicative language practice. In: 2025 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct); 2025 Oct 8-12; Daejeon, Korea. Piscataway: IEEE; 2025. p. 831-832.

[32] Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000; 55(1): 68-78.

[33] Clark A. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav Brain Sci. 2013; 36(3): 181-204.

[34] Vygotsky LS. Mind in society: Development of higher psychological processes. Cambridge: Harvard University Press; 1980.

[35] Gallagher S, Raffone A, Berkovich-Ohana A, Barendregt HP, Bauer PR, Brown KW, et al. The self-pattern and Buddhist psychology. Mindfulness. 2024; 15(4): 795-803.

[36] Wilson D, Moloney E, Parr JM, Aspinall C, Slark J. Creating an Indigenous Māori-centred model of relational health: A literature review of Māori models of health. J Clin Nurs. 2021; 30(23-24): 3539-3555.

[37] Markus H, Kitayama S. Culture and the self: Implications for cognition, emotion, and motivation. Psychol Rev. 1991; 98(2): 224-253.

[38] Varela FJ, Thompson E, Rosch E. The embodied mind, revised edition: Cognitive science and human experience. Cambridge: MIT Press; 2017.

[39] Gallagher S. How the body shapes the mind. New York: Oxford University Press; 2005.

[40] Di Paolo EA, Rohde M, De Jaegher H. Horizons for the enactive mind: Values, social interaction, and play.In: Stewart J, Gapenne O, Di Paolo EA, editors. Enaction: Toward a New Paradigm for Cognitive Science. Cambridge: The MIT Press; 2010. p. 32-87.

[41] Hohwy J. The predictive mind. New York: Oxford University Press; 2013.

[42] Ho SS, Nakamura Y, Gopang M, Swain JE. Intersubjectivity as an antidote to stress: Using dyadic active inference model of intersubjectivity to predict the efficacy of parenting interventions in reducing stress: Through the lens of dependent origination in Buddhist Madhyamaka philosophy. Front Psychol. 2022; 13: 806755.

[43] Friston K, FitzGerald T, Rigoli F, Schwartenbeck P, Pezzulo G. Active inference: A process theory. Neural Comput. 2017; 29(1): 1-49.

[44] Parr T, Pezzulo G, Friston KJ. Active inference: The free energy principle in mind, brain, and behavior. Cambridge: MIT Press; 2022.

[45] Shneiderman B. Bridging the gap between ethics and practice: Guidelines for reliable, safe, and trustworthy human-centered AI systems. ACM Trans Interact Intell Syst. 2020; 10(4): 1-31.

[46] Capel T, Brereton M. What is human-centered about human-centered AI? A map of the research landscape. In: Schmidt A, Väänänen K, Goyal T, Kristensson O, Peters A, Mueller S, Williamson JR, Wilson ML, editors. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems; 2023 Apr 23-28; Hamburg, Germany. New York: Association for Computing Machinery; 2023. p. 1-23.

[47] Amershi S, Weld D, Vorvoreanu M, Fourney A. Guidelines for human-AI interaction. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems; 2019 May 4-9; Glasgow, UK. New York: Association for Computing Machinery; 2019. p. 1-13.

[48] Noggle R. The ethics of manipulation. In: Zalta EN, editor. The Stanford Encyclopedia of Philosophy. Stanford: Stanford University; 2022.

[49] Faden R, Beauchamp T, King N. A history and theory of informed consent. New York: Oxford University Press; 1986.

[50] Raz J. The Morality of Freedom. New York: Oxford University Press; 1988.

[51] Susser D, Roessler B, Nissenbaum H. Technology, autonomy, and manipulation. Internet Policy Rev. 2019; 8(2): 1-22.

[52] Rendon-Cardona C, Burcklen MA, Legras R, Sandor C. Augmented vision systems: Paradigms and applications. IEEE Trans Visual Comput Graphics. 2025; 31(10): 9484-9501.

[53] Mori S, Ikeda S, Saito H. A survey of diminished reality: Techniques for visually concealing, eliminating, and seeing through real objects. IPSJ Trans Comput Vis Appl. 2017; 9(1): 17.

[54] Wienrich C, Latoschik ME. eXtended artificial intelligence: New prospects of human-AI interaction research. Front Virtual Real. 2021; 2: 686783.

[55] Zollmann S, Langlotz T, Grasset R, Lo WH, Mori S, Regenbrecht H. Visualization techniques in augmented reality: A taxonomy, methods and patterns. IEEE Trans Visual Comput Graphics. 2021; 27(9): 3808-3825.

[56] Dong Z, Han B, Zhang J, Wen R. An exploratory study on AI-driven visualisation techniques on decision making in extended reality. In: Viller S, Paay J, Fredericks J, Turner J, Vickery N, Wadley G, Muñoz D, Capel T, Atiq A, Davis P, Bodén M, Hardman P, Ploderer B, editors. Proceedings of the 36th Australasian Conference on Human-Computer Interaction; 2024 Nov 30-Dec 4; Brisbane, Australia. New York: Association for Computing Machinery; 2025. p. 654-664.

[57] Piumsomboon T, Lee GA, Hart JD, Ens B. Mini-me: An adaptive avatar for mixed reality remote collaboration. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems; 2018 Apr 21-26; Montreal, Canada. New York: Association for Computing Machinery; 2018. p. 1-13.

[58] Piumsomboon T, Lee GA, Ens B, Thomas BH, Billinghurst M. Superman vs giant: A study on spatial perception for a multi-scale mixed reality flying telepresence interface. IEEE Trans Vis Comput Graph. 2018; 24(11): 2974-2982.

[59] Piumsomboon T, Lee GA, Irlitti A, Ens B, Thomas BH, Billinghurst M. On the shoulder of the giant: A multi-scale mixed reality collaboration with 360 video sharing and tangible interaction. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems; 2019 May 4-9; Scotland, UK. New York: Association for Computing Machinery; 2019. p. 1-17.

[60] Katins C, Strecker J, Hinrichs J, Knierim P, Pfleging B, Kosch T. Ad-blocked reality: Evaluating user perceptions of content blocking concepts using extended reality. In: Yamashita N, Evers V, Yatani K, Ding X, Lee B, Chetty M, Toups-Dugas P, editors. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems; 2025 Apr 26-May 1; Yokohama, Japan. New York: Association for Computing Machinery; 2025. p. 1-18.

[61] Rizzo A, Hartholt A, Grimani M, Leeds A, Liewer M. Virtual reality exposure therapy for combat-related posttraumatic stress disorder. Computer. 2014; 47(7): 31-37.

[62] Wiese W. Conscious perception as augmented reality. Soc Epistemology. 2026; 40(1): 45-58.

[63] Livingston MA, Rosenblum LJ, Brown DG, Schmidt GS, Julier SJ, Baillot Y, et al. Military applications of augmented reality. In: Furht B, editor. Handbook of Augmented Reality. New York: Springer; 2011. p. 671-706.

[64] Sielhorst T, Feuerstein M, Navab N. Advanced medical displays: A literature review of augmented reality. J Display Technol. 2008; 4(4): 451-467.

[65] Bonnail E, Tseng WJ, McGill M, Lecolinet E, Huron S, Gugenheimer J. Memory manipulations in extended reality. In: Schmidt A, Väänänen K, Goyal T, Kristensson PO, Peters A, Mueller S, Williamson JR, Wilson ML, editors. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems; 2023 Apr 23-28; Hamburg, Germany. New York: Association for Computing Machinery; 2023. p. 1-20.

[66] Slater M. Place illusion and plausibility can lead to realistic behaviour in immersive virtual environments. Philos Trans R Soc Lond B Biol Sci. 2009; 364(1535): 3549-3557.

[67] Slater M, Banakou D, Beacco A, Gallego J, Macia-Varela F, Oliva R. A separate reality: An update on place illusion and plausibility in virtual reality. Front Virtual Real. 2022; 3: 914392.

[68] Triberti S, Sapone C, Riva G. Being there but where? Sense of presence theory for virtual reality applications. Humanit Soc Sci Commun. 2025; 12: 79.

[69] Skarbez R, Brooks FP, Whitton MC. Immersion and coherence: Research agenda and early results. IEEE Trans Vis Comput Graph. 2021; 27(10): 3839-3850.

[70] Rastelli C, Greco A, Kenett YN, Finocchiaro C, De Pisapia N. Simulated visual hallucinations in virtual reality enhance cognitive flexibility. Sci Rep. 2022; 12: 4027.

[71] Job M, Manoni M, Sansone LG, Viceconti A, Testa M. A surprise induced by a visual-haptic illusion in virtual reality can lead to motor improvement. Sci Rep. 2025; 15: 14741.

[72] Skinner BF. The behavior of organisms: An experimental analysis. New York: Appleton-Century-Crofts; 2019.

[73] Piumsomboon T, Ong G, Urban C, Ens B, Topliss J, Bai X, et al. Ex-Cit XR: Expert-elicitation and validation of Extended Reality visualisation and interaction techniques for disengaging and transitioning users from immersive virtual environments. Front Virtual Real. 2022; 3: 943696.

[74] Yang X, Sasikumar P, Amtsberg F, Menges A, Sedlmair M, Nanayakkara S. Who is in control? Understanding user agency in AR-assisted construction assembly. In: Yamashita N, Evers V, Yatani K, Ding X, Lee B, Chetty M, Toups-Dugas P, editors. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems; 2025 April 26-May 1; Yokohama, Japan. New York: Association for Computing Machinery; 2025. p. 1-15.

[75] Seeber I, Bittner E, Briggs RO, de Vreede T, de Vreede GJ, Elkins A, et al. Machines as teammates: A research agenda on AI in team collaboration. Inf Manag. 2020; 57(2): 103174.

[76] Zhang R, McNeese NJ, Freeman G, Musick G. “An ideal human”: Expectations of AI teammates in human-AI teaming. Proc ACM Hum-Comput Interact. 2021; 4(CSCW3): 1-25.

[77] Norman DA. The psychology of everyday things. New York: Basic Books, Inc.; 1988.

[78] Duan W, Flathmann C, McNeese N, Scalia MJ. Trusting autonomous teammates in human-AI teams - a literature review. In: Yamashita N, Evers V, Yatani K, Ding X, Lee B, Chetty M, Toups-Dugas P, editors. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems; 2025 Apr 26-May 1; Yokohama, Japan. New York: Association for Computing Machinery; 2025. p. 1-23.

[79] Han B, Dong Z, Zhang J, Wen R, Hirai T, Clark A, et al. Exploring mediation by an embodied virtual agent in immersive triadic collaborative decision-making. IEEE Trans Vis Comput Graph. 2026.

[80] Xu X, Yu A, Jonker TR, Todi K. XAIR: A framework of explainable AI in augmented reality. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. ACM, 2023: 1-30.

[81] Nam H, Kang S, Woo W, Kim K. AVAGENT: Bridging asynchronous communication through AI-powered virtual avatars. In: 2025 IEEE conference on virtual reality and 3D user interfaces abstracts and workshops (VRW); 2025 Mar 8-12; Saint Malo, France. Piscataway: IEEE; 2025. p. 1142-1146.

[82] Yang J, Tan R, Wu Q, Zheng R, Peng B, Liang Y, et al. Magma: A foundation model for multimodal AI agents. In: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2025 Jun 10-17; Nashville, USA. Piscataway: IEEE; 2025. p. 14203-14214.

[83] Liao QV, Vaughan JW. AI transparency in the age of LLMs: A human-centered research roadmap. arXiv:2306.01941 [Preprint]. 2023.

[84] Li C, Wu G, Chan GY, Turakhia DG. Satori: Towards proactive AR assistant with belief-desire-intention user modeling. In: Yamashita N, Evers V, Yatani K, Ding X, Lee B, Chetty M, Toups-Dugas P, editors. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems; 2025 Apr 26-May 1; Yokohama, Japan. New York: Association for Computing Machinery; 2025. p. 1-24.

[85] Lee M, Liang P, Yang Q. CoAuthor: Designing a human-AI collaborative writing dataset for exploring language model capabilities. In: Barbosa S, Lampe C, Appert C, Shamma DA, Drucker S, Williamson J, Yatani K, editors. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems; 2022 Apr 29-May 5; New Orleans, USA. New York: Association for Computing Machinery; 2022. p. 1-19.

[86] Nishal S, Lee M, Diakopoulos N, Wortman Vaughan J. “Helping me versus doing it for me”: Designing for agency in LLM-infused writing tools for science journalism. In: Oliver N, Shamma DA, Candello H, Cesar P, Lopes P, Bozzon A, Kosch T, Liao V, Ma X, Artizzu V, Draxler F, López G, Reinschluessel AV, Tong X, Toups Dugas PO, editors. Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems; 2026 Apr 13-17; Barcelona, Spain. New York: Association for Computing Machinery; 2026. p. 1-20.

[87] Shneiderman B. Human-centered artificial intelligence: Reliable, safe & trustworthy. Int J Hum. 2020; 36(6): 495-504.

[88] Yang Q, Steinfeld A, Rosé C, Zimmerman J. Re-examining whether, why, and how human-AI interaction is uniquely difficult to design. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems; 2020 Apr 25-30; Honolulu, USA. New York: Association for Computing Machinery; 2020. p. 1-13.

[89] Yousefi M, Crowe SE, Hoermann S, Sharifi M, Romera A, Shahi A, et al. Advancing prosociality in extended reality: Systematic review of the use of embodied virtual agents to trigger prosocial behaviour in extended reality. Front Virtual Real. 2024; 5: 1386460.

[90] Kim K, Boelling L, Haesler S, Bailenson J, Bruder G, Welch GF. Does a digital assistant need a body? The influence of visual embodiment and social behavior on the perception of intelligent virtual agents in AR. In: 2018 IEEE International Symposium on Mixed and Augmented Reality (ISMAR); 2018 Oct 16-20; Munich, Germany. Piscataway: IEEE; 2018. p. 105-114.

[91] Park JS, O’Brien J, Cai CJ, Morris MR, Liang P, Bernstein MS. Generative agents: Interactive simulacra of human behavior. In: Follmer S, Han J, Steimle J, Riche NH, editors. Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology; 2023 Oct 29-Nov 1; San Francisco, USA. New York: Association for Computing Machinery; 2023. p. 1-22.

[92] Behrouz A, Razaviyayn M, Zhong P, Mirrokni V. Nested learning: The illusion of deep learning architectures. arXiv:2512.24695 [Preprint]. 2025.

[93] Horvitz E. Principles of mixed-initiative user interfaces. In: Proceedings of the SIGCHI conference on Human Factors in Computing Systems; 1999 May 15-20; Pittsburgh, USA. New York: Association for Computing Machinery; 1999. p. 159-166.

[94] Bradshaw JM, Sierhuis M, Acquisti A, Feltovich P, Hoffman R, Jeffers R, et al. Adjustable autonomy and human-agent teamwork in practice: An interim report on space applications. In: Hexmoor H, Castelfranchi C, Falcone R, editors. Agent autonomy. Boston: Springer; 2003. p. 243-280.

[95] Greunke L, Sadagic A. Taking immersive VR leap in training of landing signal officers. IEEE Trans Vis Comput Graph. 2016; 22(4): 1482-1491.

[96] Orlosky J, Sra M, Bektaş K, Peng H, Kim J, Kos’myna N, et al. Telelife: The future of remote living. Front Virtual Real. 2021; 2: 763340.

[97] Jing A, May K, Lee G, Billinghurst M. Eye see what you see: Exploring how bi-directional augmented reality gaze visualisation influences co-located symmetric collaboration. Front Virtual Real. 2021; 2: 697367.

[98] Turkle S. Alone together: Why we expect more from technology and less from each other. New York: Basic Books; 2011.

[99] Dreyfus SE. The five-stage model of adult skill acquisition. Bull Sci Technol Soc. 2004; 24(3): 177-181.

[100] Lee GA, Teo T, Kim S, Billinghurst M. A user study on MR remote collaboration using live 360 video. In: 2018 IEEE International Symposium on Mixed and Augmented Reality (ISMAR); 2018 Oct 16-20; Munich, Germany. Piscataway: IEEE; 2018. p. 153-164.

[101] Oda O, Elvezio C, Sukan M, Feiner S, Tversky B. Virtual replicas for remote assistance in virtual and augmented reality. In: Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology; 2015 Nov 11-15; Charlotte, USA. New York: Association for Computing Machinery; 2015. p. 405-415.

[102] Huang G, Qian X, Wang T, Patel F. AdapTutAR: An adaptive tutoring system for machine tasks in augmented reality. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems; 2021 May 8-13; Yokohama, Japan. New York: Association for Computing Machinery; 2021. p. 1-15.

[103] Anderson JR, Corbett AT, Koedinger KR, Pelletier R. Cognitive tutors: Lessons learned. J Learn Sci. 1995; 4(2): 167-207.

[104] Vanneste P, Huang Y, Park JY, Cornillie F, Decloedt B, Van den Noortgate W. Cognitive support for assembly operations by means of augmented reality: An exploratory study. Int J Hum Comput Stud. 2020; 143: 102480.

[105] Buchner J, Buntins K, Kerres M. The impact of augmented reality on cognitive load and performance: A systematic review. J Comput Assist Learn. 2022; 38(1): 285-303.

[106] Atkinson RK, Maier UH. From studying examples to solving problems: Fading worked-out solution steps helps learning. In: Proceedings of the Twenty-second Annual Conference of the Cognitive Science Society; 2000 Aug 13-15; Philadelphia: University of Pennsylvania. UK: Psychology Press; 2000. Available from: https://escholarship.org/uc/item/81b9j9hs

[107] Sweller J, Ayres P, Kalyuga S. The guidance fading effect. In: Cognitive load theory. New York: Springer; 2011. p. 171-182.

[108] Schmidt RA. A schema theory of discrete motor skill learning. Psychol Rev. 1975; 82(4): 225-260.

[109] Raviv L, Lupyan G, Green SC. How variability shapes learning and generalization. Trends Cogn Sci. 2022; 26(6): 462-483.

[110] Domínguez A, Pacho G, Bowers L, Wild F, Alcock S, Chiazzese G, et al. Dataset of user interactions across four large pilots on the use of augmented reality in learning experiences. Sci Data. 2023; 10(1): 823.

[111] Cho H, Chang E, Yuan B, Teo T, Lee GA, Piumsomboon T, et al. Bichronous collaboration: Using spatiotemporal cues to collaborate across time and space on physical tasks. In: 2025 IEEE international symposium on mixed and augmented reality (ISMAR); 2025 Oct 8-12; Daejeon, Korea. Piscataway: IEEE; 2025. p. 1398-1408.

[112] Yang U, Kim GJ. Implementation and evaluation of “just follow me”: An immersive, VR-based, motion-training system. Presence Teleoperators Virtual Environ. 2002; 11(3): 304-323.

[113] Jarc AM, Stanley AA, Clifford T, Gill IS, Hung AJ. Proctors exploit three-dimensional ghost tools during clinical-like training scenarios: A preliminary study. World J Urol. 2017; 35(6): 957-965.

[114] Piumsomboon T, Altimira D, Kim H, Clark A, Lee G, Billinghurst M. Grasp-Shell vs gesture-speech: A comparison of direct and indirect natural interaction techniques in augmented reality. In: 2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR); 2014 Sep 10-12; Munich, Germany. Piscataway: IEEE; 2014, p. 73-82.

[115] Limbu BH, Jarodzka H, Klemke R, Specht M. Using sensors and augmented reality to train apprentices using recorded expert performance: A systematic literature review. Educ Res Rev. 2018; 25: 1-22.

[116] Kirschner PA, Sweller J, Kirschner F, Zambrano RJ. From cognitive load theory to collaborative cognitive load theory. Intern J Comput-Support Collab Learn. 2018; 13(2): 213-233.

[117] Renkl A. The worked examples principle in multimedia learning. In: The Cambridge handbook of multimedia learning. Cambridge: Cambridge University Press; 2014. p. 391-412.

[118] Collins A, Brown JS, Newman SE. Cognitive apprenticeship: Teaching the Crafts of reading, writing, and mathematics. In: Resnick LB, editor. Knowing, learning, and instruction. Hillsdale: Lawrence Erlbaum Associates; 2018. p. 453-494.

[119] Csikszentmihalyi M. Flow: The psychology of optimal experience. J Leis Res. 1992; 24(1): 93-94.

[120] Campero A, Raileanu R, Küttler H, Tenenbaum JB, Rocktäschel T, Grefenstette E. Learning with AMIGo: Adversarially motivated intrinsic goals. arXiv:2006.12122 [Preprint]. 2020.

[121] Bjork EL, Bjork RA. Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In: Gernsbacher MA, Pomerantz J, editors. Psychology and the real world: Essays illustrating fundamental contributions to society; New York: Worth Publishing; 2014. p. 59-68. Available from: https://jacobzelko.com/05252020211350-hard-on-self/

[122] Landman A, van Oorschot P, René van Paassen MMR, Groen EL, Bronkhorst AW, Mulder M. Training pilots for unexpected events: A simulator study on the advantage of unpredictable and variable scenarios. Hum Factors. 2018; 60(6): 793-805.

[123] Ericsson KA, Krampe RT, Tesch-Römer C. The role of deliberate practice in the acquisition of expert performance. Psychol Rev. 1993; 100(3): 363-406.

[124] Sarter NB, Woods DD. How in the world did we ever get into that mode? Mode error and awareness in supervisory control. Hum Factors. 1995; 37(1): 5-19.

[125] Eom H, Lee SH. Mode confusion of human-machine interfaces for automated vehicles. J Comput Des Eng. 2022; 9(5): 1995-2009.

[126] Lyons JB, Sycara K, Lewis M, Capiola A. Human-autonomy teaming: Definitions, debates, and directions. Front Psychol. 2021; 12: 589585.

[127] Wischnewski M, Krämer N, Müller E. Measuring and understanding trust calibrations for automated systems: A survey of the state-of-the-art and future directions. In: Schmidt A, Väänänen K, Goyal T, Kristensson PO, Peters A, Mueller S, Williamson JR, Wilson ML, editors. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems; Hamburg, Germany. New York: Association for Computing Machinery; 2023. p. 1-16.

[128] Okamura K, Yamada S. Adaptive trust calibration for human-AI collaboration. PLoS One. 2020; 15(2): e0229132.

[129] Beer JM, Fisk AD, Rogers WA. Toward a framework for levels of robot autonomy in human-robot interaction. J Hum Robot Interact. 2014; 3(2): 74-99.

[130] Doudkin A, Pataranutaporn P, Maes P. AI persuading AI vs AI persuading humans: LLMs' differential effectiveness in promoting pro-environmental behavior. arXiv:2503.02067 [Preprint]. 2025.

[131] Mathur A, Acar G, Friedman MJ, Lucherini E, Mayer J, Chetty M, et al. Dark patterns at scale: Findings from a crawl of 11K shopping websites. Proc ACM Hum-Comput Interact. 2019; 3: 1-32.

[132] Luguri J, Strahilevitz LJ. Shining a light on dark patterns. J Leg Anal. 2021; 13(1): 43-109.

[133] Thaler RH, Sunstein CR. Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press. 2008.

[134] Sunstein CR. Nudging and choice architecture: Ethical considerations. Yale J Regul. 2015. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:17915544

[135] Schmidt AT, Engelen B. The ethics of nudging: An overview. Philos Compass. 2020; 15(4): e12658.

[136] Tonnis M, Klein L, Klinker G. Perception thresholds for augmented reality navigation schemes in large distances. In: 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality; 2008 Sep 15-18; Cambridge, UK. Piscataway: IEEE; 2008. p. 189-190.

[137] Kim S, Dey AK. Simulated augmented reality windshield display as a cognitive mapping aid for elder driver navigation. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; 2009 Apr 4-9; Boston, USA. New York: Association for Computing Machinery; 2009. p. 133-142.

[138] Meske C, Amojo I. Ethical guidelines for the construction of digital nudges. arXiv:2003.05249v1 [Preprint]. 2020.

[139] Sorensen T, Moore J, Fisher J, Gordon M, Mireshghallah N, Rytting CM, et al. A roadmap to pluralistic alignment. arXiv:2402.05070 [Preprint]. 2024.

[140] Reicherts L, Zhang ZT, von Oswald E, Liu Y, Rogers Y, Hassib M. AI, help me think: But for myself: Assisting people in complex decision-making by providing different kinds of cognitive support. In: Yamashita N, Evers V, Yatani K, Ding X, Lee B, Chetty M, Toups-Dugas P, editors. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems; 2025 Apr 26-May 1; Yokohama, Japan. New York: Association for Computing Machinery; 2025. p. 1-19.

[141] Haque AB, Islam AKMN, Mikalef P. Explainable Artificial Intelligence (XAI) from a user perspective: A synthesis of prior literature and problematizing avenues for future research. Technol Forecast Soc Change. 2023; 186: 122120.

[142] Steyvers M, Kumar A. Three challenges for AI-assisted decision-making. Perspect Psychol Sci. 2024; 19(5): 722-734.

[143] Krakowski S. Human-AI agency in the age of generative AI. Inf Organ. 2025; 35(1): 100560.

[144] Endsley MR. From here to autonomy: Lessons learned from human-automation research. Hum Factors. 2017; 59(1): 5-27.

[145] Endsley MR, Kiris EO. The out-of-the-loop performance problem and level of control in automation. Hum Factors. 1995; 37(2): 381-394.

[146] Cheng EC, Cheng J, Siu A. Toward safe and responsible AI agents: A three-pillar model for transparency, accountability, and trustworthiness. arXiv:2601.06223 [Preprint]. 2026.

[147] Kaber DB, Endsley MR. Out-of-the-loop performance problems and the use of intermediate levels of automation for improved control system functioning and safety. Process Saf Prog. 1997; 16(3): 126-131.

[148] Johnson M, Bradshaw JM, Feltovich PJ, Jonker CM, van Riemsdijk B, Sierhuis M. The fundamental principle of coactive design: Interdependence must shape autonomy. In: De Vos M, Fornara N, Pitt JV, Vouros G, editors. Coordination, Organizations, Institutions, and Norms in Agent Systems VI. Berlin: Springer; 2011. p. 172-191.

[149] Mathieu JE, Heffner TS, Goodwin GF, Salas E, Cannon-Bowers JA. The influence of shared mental models on team process and performance. J Appl Psychol. 2000; 85(2): 273-283.

[150] De Dreu CKW, Weingart LR. Task versus relationship conflict, team performance, and team member satisfaction: A meta-analysis. J Appl Psychol. 2003; 88(4): 741-749.

[151] DeChurch LA, Mesmer-Magnus JR. The cognitive underpinnings of effective teamwork: A meta-analysis. J Appl Psychol. 2010; 95(1): 32-53.

[152] Weick KE. Sensemaking in organizations. Thousand Oaks: SAGE Publications, Inc; 1995.

[153] Chen H, Wang P, Hao S. AI in the spotlight: The impact of artificial intelligence disclosure on user engagement in short-form videos. Comput Hum Behav. 2025; 162: 108448.

[154] Lukosch S, Billinghurst M, Alem L, Kiyokawa K. Collaboration in augmented reality. Comput Supported Coop Work. 2015; 24(6): 515-525.

[155] Piumsomboon T, Dey A, Ens B, Lee G, Billinghurst M. The effects of sharing awareness cues in collaborative mixed reality. Front Robot AI. 2019; 6: 5.

[156] Kim S, Lee G, Huang W, Kim H, Woo W, Billinghurst M. Evaluating the combination of visual communication cues for HMD-based mixed reality remote collaboration. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems; 2019 May 4-9; Glasgow, UK. New York: Association for Computing Machinery; 2019. p. 1-13.

[157] Kim T, Chang A, Holland L, Pentland AS. Meeting mediator: Enhancing group collaboration using sociometric feedback. In: Proceedings of the 2008 ACM conference on Computer supported cooperative work; 2008 Nov 8-12; San Diego, USA. New York: Association for Computing Machinery; 2008. p. 457-466.

[158] Templeton EM, Chang LJ, Reynolds EA, Cone LeBeaumont MD, Wheatley T. Fast response times signal social connection in conversation. Proc Natl Acad Sci U S A. 2022; 119(4): e2116915119.

[159] De Freitas J, Oğuz-Uğuralp Z, Uğuralp AK, Puntoni S. AI companions reduce loneliness. J Consum Res. 2026; 52(6): 1126-1148.

[160] Deci EL, Ryan RM. Self-determination theory. In: Lange PAMV, Kruglanski AW, Higgins ET, editors. Handbook of Theories of Social Psychology. Thousand Oaks: SAGE Publications; 2012. p. 416-436.

[161] Peck TC, Seinfeld S, Aglioti SM, Slater M. Putting yourself in the skin of a black avatar reduces implicit racial bias. Conscious Cogn. 2013; 22(3): 779-787.

[162] Slater M, Gonzalez-Liencres C, Haggard P, Vinkers C, Gregory-Clarke R, Jelley S, et al. The ethics of realism in virtual and augmented reality. Front Virtual Real. 2020; 1: 1.

[163] Pataranutaporn P, Winson K, Yin P, Lapapirojn A, Ouppaphan P, Lertsutthiwong M, et al. Future you: A conversation with an AI-generated future self reduces anxiety, negative emotions, and increases future self-continuity. In: 2024 IEEE Frontiers in Education Conference (FIE); 2024 Oct 13-16; Washington, USA. Piscataway: IEEE; 2024. p. 1-10.

[164] Hershfield HE, Goldstein DG, Sharpe WF, Fox J, Yeykelis L, Carstensen LL, et al. Increasing saving behavior through age-progressed renderings of the future self. J Mark Res. 2011; 48: S23-S37.

[165] Chen VHH, Ibasco GC. All it takes is empathy: How virtual reality perspective-taking influences intergroup attitudes and stereotypes. Front Psychol. 2023; 14: 1265284.

[166] Markowitz DM, Laha R, Perone BP, Pea RD, Bailenson JN. Immersive virtual reality field trips facilitate learning about climate change. Front Psychol. 2018; 9: 2364.

[167] Herrmann T, Pfeiffer S. Keeping the organization in the loop: A socio-technical extension of human-centered artificial intelligence. AI & Soc. 2023; 38(4): 1523-1542.

[168] Leofante F, Ayoobi H, Dejl A, Freedman G, Gorur D, Jiang J, et al. Contestable AI needs computational argumentation. In: Marquis P, Ortiz M, Pagnucco M, editors. Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning; 2024 Nov 2-8; Hanoi, Vietnam. California: IJCAI Organization; 2024. p. 888-896.

[169] Ashton H. Causal Campbell-Goodhart's law and Reinforcement Learning. arXiv:2011.01010 [Preprint]. 2020.

[170] Karwowski J, Hayman O, Bai X, Kiendlhofer K, Griffin C, Skalse J. Goodhart's law in reinforcement learning. arXiv:2310.09144 [Preprint]. 2023.

[171] Macy JR. Dependent co-arising: The distinctiveness of Buddhist ethics. J Relig Ethics. 1979; 7(1): 38-52. Available from: http://www.jstor.org/stable/40018242

[172] Buxton B. Sketching user experiences: Getting the design right and the right design. San Francisco: Morgan Kaufmann Publishers Inc.; 2010.

[173] Höök K, Löwgren J. Strong concepts: Intermediate-level knowledge in interaction design research. ACM Trans Comput-Hum Interact. 2012; 19(3): 1-18.

[174] Gaver W. What should we expect from research through design? In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; 2012 May 5-10; Austin, USA. New York: Association for Computing Machinery; 2012. p. 937-946.

[175] Dourish P. Implications for design. In: Grinter R, Rodden T, Aoki P, Cutrell E, Jeffries R, Olson G, editors. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; 2006 Apr 22-27; Montréal, Canada. New York: Association for Computing Machinery; 2006. p. 541-550.

[176] Carroll JM, Rosson MB. Getting around the task-artifact cycle: How to make claims and design by scenario. ACM Trans Inf Syst. 1992; 10(2): 181-212.

[177] Gregor S, Jones D. The anatomy of a design theory. J Assoc Inf Syst. 2007; 8(5): 312-335.

[178] Lee JD, See KA. Trust in automation: Designing for appropriate reliance. Hum Factors. 2004; 46(1): 50-80.

[179] Davidson K, Lisle L, Whitley K, Bowman DA, North C. Exploring the evolution of sensemaking strategies in immersive space to think. IEEE Trans Vis Comput Graph. 2023; 29(12): 5294-5307.

[180] Hart JD, Piumsomboon T, Lee GA, Smith RT, Billinghurst M. Manipulating avatars for enhanced communication in extended reality. In: 2021 IEEE International Conference on Intelligent Reality (ICIR); 2021 May 12-13; Piscataway, USA. Piscataway: IEEE; 2021, p. 9-16.

[181] Kitson A, Chirico A, Gaggioli A. A review on research and evaluation methods for investigating self-transcendence. Front Psychol. 2020; 11: 547687.

[182] Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, et al. Mastering the game of Go without human knowledge. Nature. 2017; 550(7676): 354-359.

[183] Russell S. Human compatible: Artificial intelligence and the problem of control. New York: Viking; 2019.

[184] Amodei D, Olah C, Steinhardt J, Christiano P, Schulman J, Mané D. Concrete problems in AI safety. arXiv:1606.06565v2 [Preprint]. 2016.

[185] Burns C, Izmailov P, Kirchner JH, Baker B, Gao L, Aschenbrenner L, et al. Weak-to-strong generalization: Eliciting strong capabilities with weak supervision. arXiv:2312.09390v1 [Preprint]. 2023.

[186] Olah C, Mordvintsev A, Schubert L. Feature visualization. Distill. 2017; 2(11): e7.

[187] Olah C, Satyanarayan A, Johnson I, Carter S, Schubert L, Ye K, et al. The building blocks of interpretability. Distill. 2018; 3(3): e10.

[188] Christiano PF, Leike J, Brown T. Deep reinforcement learning from human preferences. In: von Luxburg U, Guyon I, Bengio S, Wallach H, Fergus R, editors. Proceedings of the 31st International Conference on Neural Information Processing Systems; 2017 Dec 4-9; California, USA. United States: Curran Associates Inc.; 2017. p. 4302-4310.

[189] Kruger J, Dunning D. Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. J Pers Soc Psychol. 1999; 77(6): 1121-1134.

[190] Lu F, Zhao Q. Towards cobodied/symbodied AI: Concept and eight scientific and technical problems. Sci China Inf Sci. 2026; 69: 116101.

[191] Zhao S, Tan F, Fennedy K. Heads-up computing moving beyond the device-centered paradigm. Commun ACM. 2023; 66(9): 56-63.