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Matjaž Kljun HCI Perspective on Meaning-Making in the Human-AI
University of Primorska, Slovenia Loop
matjaz.kljun@upr.si
©2026MatjažKljun This presentation examines meaning-making in the human-AI loop from an HCI
perspective. Contemporary communication is increasingly multi-code (Kress,
2009). Meaning is produced and interpreted through intertwined language, visu-
als, sound, movement, spatial layout, and interactivity. From a Human-Computer
Interaction(HCI)perspective,multidimensional,multi-codeliteracycanbeframed
as users’ ability to (1) recognize layered semiotic resources in digital artifacts (e.g.,
text, visualization, temporality and feedback loops), (2) understand how inter-
faces and algorithms shape attention, interpretation, and decision-making, and
(3) critically judge when meaning-making is human-led versus system-driven.
Generative AI introduces a distinctive challenge as it does not ‘understand’ mean-
ing in a human sense, but statistically constructs outputs from learned patterns
of use. Human-AI interaction thus becomes a form of co-authorship: users ex-
press intentions, AI systems generate candidates, and interface affordances (e.g.,
versioning, source attribution, uncertainty indicators, explanations) determine
whether reading/writing processes are transparent or drift towards perceived au-
thority and an illusion of understanding. Consequently, multi-code literacy today
also includes literacy in interpreting system signals: uncertainty, probability, data
bias, hallucinations, and calibrated trust.
This contribution proposes an HCI framework for studying meaning formation as
an interaction process across four dimensions: (a) representations (which codes
are present), (b) transformations (how AI/interface pipelines translate input into
output), (c) interpretation (how users build mental models and explanations),
and (d) responsibility (traceability, attribution, and ethics) (Norman, 2013; Ovi-
att et al., 2003; Hutchins, 1995; Amershi et al., 2019). Building on this framework,
the contribution outlines research directions and design implications for educa-
tionalcontexts:tools that makeprovenance,confidence,and alternatives explicit,
and learning practices that foster reflection on how meaning is negotiated in the
human-technology loop.
Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., S.
Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019).
Guidelines for human-AI interaction. In CHI ’19: Proceedings of the 2019 CHI
conference on human factors in computing systems (paper n. 3). Association
for Computing Machinery.
Hutchins, E. (1995). Cognition in the wild. MIT Press.
Kress, G. (2009). Multimodality: A social semiotic approach to contemporary com-
munication. Routledge.
Norman, D. (2013). The design of everyday things: Revised and expanded edition. Ba-
sic books.
Oviatt,S.,Coulston,R.,Tomko,S.,Xiao,B., Lunsford,R., Wesson, M.,& Carmichael, L.
Meaning-Making, Multiliteracies
(2003). Toward a theory of organized multimodal integration patterns during
and Multimodality
Abstracts of the International human-computer interaction. In ICMI ’03: Proceedings of the 5th international
Symposium conference on Multimodal interfaces (pp. 44–51). Association for Computing
Koper, 19–20 March 2026 Machinery.
https://doi.org/10.26493/978-961-293-565-8.12 15
