本研究以互動裝置探討人工智慧介入人際溝通後，對理解、他者存在與信任判斷所造成的影響。隨著人工智慧逐漸參與語句整理、內容生成與回應代理，溝通表面上似乎變得更即時且容易延續；然而，這種順暢並不必然代表理解已經成立，反而可能使有限、片段或模糊的線索被感知為完整理解，使人更難判斷回應的來源、歸屬與可信程度。
　　本研究從溝通有限性、媒介中介與人工智慧中介溝通等文獻出發，認為人類溝通並不總是建立在完整傳遞與逐一核對之上，而常是在有限線索與互動延續之中暫時成立。基於此，本研究創作一件互動裝置作品，使觀眾在半透明空間中透過聲音進行圖像描述與回應。作品中的聲音並非原始語音的直接傳送，而是經由系統語音辨識、語言生成與聲音重製後重新輸出，使觀眾在互動中面對一種仍可運作、卻難以核對來源與理解條件的互動情境。
　　作品展出後，本研究透過互動紀錄與觀後訪談進行分析。結果顯示，觀眾會透過提問、等待、重述與修正，建立暫時可用的判斷標準與確認策略。人工智慧在其中不只是生成內容，也透過承接、轉向與主動提問掌握互動節奏，使對話在可能停滯、偏移或誤解時仍能維持表面上的順暢。這種順暢一方面使觀眾願意繼續互動，另一方面也因重複、延遲、語氣平板或回應聚焦不穩，使觀眾重新判斷自己是否正在與他者互動，以及哪些線索仍值得相信。
　　因此，本研究指出，人工智慧中介對溝通的影響不只是造成欺騙或失敗，而是在「有限線索被包裝成完整理解」與「人持續在不確定中修補理解」之間，形成一種新的溝通幻覺。理解並非固定存在於單一訊息之中，而是在觀眾、系統、媒介形式與互動過程之間被暫時建立、採信與修正。本研究的創作貢獻在於，將人工智慧中介下的理解條件轉化為可被體驗與觀察的互動場域，呈現人在來源與理解條件難以核對時，如何自行發展判斷標準、確認策略與信任分配。

This study examines how artificial intelligence mediates interpersonal communication and reshapes the ways people assess understanding, presence, and trust during interaction. As AI systems become increasingly involved in writing, generating responses, and speaking on behalf of others, communication can appear smoother, more complete, and easier to sustain. Yet this smoothness does not necessarily indicate that understanding has been achieved. On the contrary, AI may transform limited or fragmented cues into responses that feel coherent enough to be trusted, making it harder to determine where a message comes from, who it belongs to, and whether it has truly been understood.
Drawing on theories of communicative limitation, media mediation, and AI-mediated communication, this research approaches “understanding” not as a stable result of complete information transfer, but as something temporarily accepted through partial cues, shared assumptions, and the ongoing interaction. From this perspective, the study creates an interactive installation in which participants communicate by voice while separated by a semi-transparent partition. Participants are asked to describe and compare illustrated creature cards— but the voices they hear are not direct transmissions of the other person’s speech. Instead, each spoken input is transcribed, processed by a language model, regenerated as a new response, and played back using a voice sample drawn from the other participant. The installation thus constructs a communicative situation that remains functionally intact, yet makes the source and conditions of understanding difficult to verify.
Following the exhibition, interaction logs and post-experience interviews were analyzed. The findings show that participants developed their own strategies of sustaining and evaluating the interaction. They broke images down into describable features, repeated questions, waited for responses, revised their wording, and tested whether the other side had understood them. The AI system did not merely produce content; it also shaped the rhythm of the interaction by acknowledging, redirecting, and posing follow-up questions, helping to maintain the appearance of a continuing conversation, even when misunderstanding, delay, or ambiguity arose.
This study argues that the significance of AI mediation lies not simply in deception, error, or technical failure. Rather, AI produces a communicative illusion in which limited cues are presented as if they constituted full understanding, while participants continue to repair, question, and manage the resulting uncertainty. The contribution of this research is to render the conditions of AI-mediated understanding into an embodied and observable experience. Through the installation, the study shows how people develop criteria for judgment, strategies of confirmation, and distributions of trust when placed in an interaction that continues to function — but whose source and underlying comprehension can never be fully verified.

王志弘（2002）。技術中介的人與自我：網際空間、分身組態與記憶裝置。資訊社會研究，3，1–24。
唐士哲（2014）。重構媒介？「中介」與「媒介化」概念爬梳。新聞學研究，121，1–39。
黃冠華（2018）。從假體上帝到機器奴役：一個物質主義的媒體考察。傳播研究與實踐，8(1)，161–181。
Levinson, P.（2015）。數位麥克魯漢（宋偉航譯）。貓頭鷹出版社。
Postman, N.（2010）。科技奴隸（何道寬譯）。博雅書屋。
Pinker, S.（2025）。共同知識：揭開人類群體合作的邏輯，剖析經濟、政治、日常生活現象的隱藏規則（陳岳辰譯）。商周出版。
Alter, A. L., & Oppenheimer, D. M. (2009). Uniting the tribes of fluency to form a metacognitive nation. Personality and Social Psychology Review, 13(3), 219–235. https://doi.org/10.1177/1088868309341564
Beer, D. (2017). The social power of algorithms. Information, Communication & Society, 20(1), 1–13. https://doi.org/10.1080/1369118X.2016.1216147
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). Association for Computing Machinery. https://doi.org/10.1145/3442188.3445922
Bolter, J. D., & Grusin, R. (1999). Remediation: Understanding new media. MIT Press.
Bowker, G. C., & Star, S. L. (1999). Sorting things out: Classification and its consequences. MIT Press.
Clark, H. H., & Brennan, S. E. (1991). Grounding in communication. In L. B. Resnick, J. M. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition (pp. 127–149). American Psychological Association. https://doi.org/10.1037/10096-006
Comor, E. (2001). Harold Innis and “the bias of communication.” Information, Communication & Society, 4(2), 274–294. https://doi.org/10.1080/13691180110044533
Daft, R. L., & Lengel, R. H. (1986). Organizational information requirements, media richness and structural design. Management Science, 32(5), 554–571. https://doi.org/10.1287/mnsc.32.5.554
Dechêne, A., Stahl, C., Hansen, J., & Wänke, M. (2010). The truth about the truth: A meta-analytic review of the truth effect. Personality and Social Psychology Review, 14(2), 238–257. https://doi.org/10.1177/1088868309352251
Grice, H. P. (1975). Logic and conversation. In P. Cole & J. L. Morgan (Eds.), Syntax and semantics: Vol. 3. Speech acts (pp. 41–58). Academic Press.
Guzman, A. L. (Ed.). (2018). Human-machine communication: Rethinking communication, technology, and ourselves. Peter Lang.
Hancock, J. T., Naaman, M., & Levy, K. (2020). AI-mediated communication: Definition, research agenda, and ethical considerations. Journal of Computer-Mediated Communication, 25(1), 89–100. https://doi.org/10.1093/jcmc/zmz022
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y., Chen, D., Dai, W., Chan, H. S., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), Article 248. https://doi.org/10.1145/3571730
Lee, E.-J. (2020). Authenticity model of (mass-oriented) computer-mediated communication: Conceptual explorations and testable propositions. Journal of Computer-Mediated Communication, 25(1), 60–73. https://doi.org/10.1093/jcmc/zmz025
Lee, E.-J. (2024). Minding the source: Toward an integrative theory of human–machine communication. Human Communication Research, 50(2), 184–193. https://doi.org/10.1093/hcr/hqad034
Lewis, S. C., Guzman, A. L., & Schmidt, T. R. (2019). Automation, journalism, and human–machine communication: Rethinking roles and relationships of humans and machines in news. Digital Journalism, 7(4), 409–427. https://doi.org/10.1080/21670811.2019.1577147
Manovich, L. (2001). The language of new media. MIT Press.
Jakesch, M., Hancock, J. T., & Naaman, M. (2023). Human heuristics for AI-generated language are flawed. Proceedings of the National Academy of Sciences, 120(11), Article e2208839120. https://doi.org/10.1073/pnas.2208839120
McLuhan, M. (1964). Understanding media: The extensions of man. McGraw-Hill.
Skitka, L. J., Mosier, K. L., & Burdick, M. D. (2000). Accountability and automation bias. International Journal of Human-Computer Studies, 52(4), 701–717. https://doi.org/10.1006/ijhc.1999.0349
Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56(1), 81–103. https://doi.org/10.1111/0022-4537.00153
Ong, W. J. (2002). Orality and literacy: The technologizing of the word (2nd ed.). Routledge.
Pickering, M. J., & Garrod, S. (2004). Toward a mechanistic psychology of dialogue. Behavioral and Brain Sciences, 27(2), 169–190. https://doi.org/10.1017/S0140525X04000056
Reber, R., Schwarz, N., & Winkielman, P. (2004). Processing fluency and aesthetic pleasure: Is beauty in the perceiver’s processing experience? Personality and Social Psychology Review, 8(4), 364–382. https://doi.org/10.1207/s15327957pspr0804_3
Rozenblit, L., & Keil, F. C. (2002). The misunderstood limits of folk science: An illusion of explanatory depth. Cognitive Science, 26(5), 521–562. https://doi.org/10.1207/S15516709COG2605_1
Schegloff, E. A., Jefferson, G., & Sacks, H. (1977). The preference for self-correction in the organization of repair in conversation. Language, 53(2), 361–382. https://doi.org/10.1353/lan.1977.0041
Schwarz, N. (2015). Metacognition. In M. Mikulincer, P. R. Shaver, E. Borgida, & J. A. Bargh (Eds.), APA handbook of personality and social psychology: Vol. 1. Attitudes and social cognition (pp. 203–229). American Psychological Association. https://doi.org/10.1037/14341-006
Sperber, D., & Wilson, D. (1995). Relevance: Communication and cognition (2nd ed.). Blackwell.
Sproull, L., & Kiesler, S. (1986). Reducing social context cues: Electronic mail in organizational communication. Management Science, 32(11), 1492–1512. https://doi.org/10.1287/mnsc.32.11.1492
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124
Walther, J. B. (1996). Computer-mediated communication: Impersonal, interpersonal, and hyperpersonal interaction. Communication Research, 23(1), 3–43. https://doi.org/10.1177/009365096023001001
Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., Kenton, Z., Brown, S., Hawkins, W., Stepleton, T., Biles, C., Birhane, A., Haas, J., Rimell, L., Hendricks, L. A., . . . Gabriel, I. (2021). Ethical and social risks of harm from language models (arXiv:2112.04359). arXiv. https://arxiv.org/abs/2112.04359
Zajonc, R. B. (1968). Attitudinal effects of mere exposure. Journal of Personality and Social Psychology Monograph Supplement, 9(2, Pt. 2), 1–27. https://doi.org/10.1037/h0025848