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CHI '26 · Honorable mention · full-paper review · confidence medium-high

Mapping the Landscape of Affective Extended Reality: A Scoping Review of Biodata-Driven Systems for Understanding and Sharing Emotions

Zhidian Lin , Allison Jing , Ziyuan Qu , Fabio Zambetta , Ryan M. Kelly

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This is a solid CHI synthesis paper: it does not claim a new sensing method, but it does something valuable by naming affective XR and organizing a fragmented literature into a usable taxonomy. The contribution is strongest as a field map and research agenda, with evidence that matches the scope of a scoping review.

Axes Lens

Rare contribution shape, typical evidence profile. The point here is not a score. It is to show what kind of claim the paper makes, and whether the evidence pattern is unusual or baseline in this 268 -review set.

Review Summary

This paper reads as a strong CHI synthesis contribution rather than a prototype or empirical systems paper. Its main move is to name and delimit “affective XR” as a category of XR systems that collect biodata, transform it into emotion representations, and feed it back to influence experience and behavior. That framing is useful because it turns a scattered set of biodata-driven emotion-sharing systems into a coherent design space. The paper’s novelty is therefore not in a new sensing pipeline or interaction technique, but in the combination of a field-level label, a taxonomy of biodata flows, nine design dimensions for representing emotions, and four design challenges. The validation is appropriate to that goal: a scoping review of 82 papers, with analysis of technologies, interaction techniques, and evaluation methods. For a synthesis paper, that is the right kind of evidence. The main limitation is also clear from the method: the paper can map what exists, but it cannot prove that affective XR designs work better than alternatives, nor can it fully support claims about underexplored directions beyond the reviewed corpus. The discussion of rare patterns such as deceptive, variable, collective, and interactive displays is useful as agenda-setting, but those claims should be read as corpus-based observations rather than exhaustive statements about the whole design space. Overall, the paper’s contribution is credible, field-shaping, and well aligned with its evidence base.

What Changed

Canon before

Prior CHI work on biodata-driven XR appears fragmented across individual systems and interaction techniques rather than consolidated as a named affective XR landscape.

Departure from common sense

The paper treats emotion sharing in XR as a biodata-mediated design problem: systems collect physiological signals, transform them into emotion representations, and display feedback to influence users’ behaviour and experiences. That framing departs from a naive view that emotion communication in XR is mainly a matter of direct expression or interface aesthetics.

Actual novelty

The paper’s novelty is primarily synthetic and framing-oriented: it introduces the notion of affective XR and then maps the literature with a taxonomy of biodata flows, nine design dimensions for representing emotions, and four design challenges. The contribution is not a new sensing technique or prototype, but a structured synthesis of an existing but scattered body of work.

Evidence

The paper validates its claims through a scoping review of 82 papers on biodata, emotions, and XR, analyzing technologies, interaction techniques, and evaluation methods. The evidence supports a field-level mapping and taxonomy rather than causal or performance claims about a specific system.

Limits

Method limits

The method is a scoping review, so it can map and categorize the literature but cannot establish causal effectiveness of any particular affective XR design. Its conclusions depend on the completeness and coding choices of the reviewed corpus.

Deployment limits

The mapping is most useful for researchers and designers working on biodata-driven XR emotion systems; it does not directly prescribe deployable interaction rules for all XR contexts or guarantee transfer to non-XR emotion-sharing settings.

Boundary conditions

The review’s underexplored areas suggest the landscape is uneven: deceptive designs, variable data displays, collective representations of emotion, and interactive data displays are rare in the corpus, so the taxonomy is strongest for the more common design patterns represented in the 82 papers.

Manual check: Verify exact wording of the taxonomy and design-challenge counts against the full text if publication-grade precision is required.

Position in field

This is a field-mapping CHI paper that consolidates a scattered literature into a named area and design taxonomy. Its value is in synthesis, vocabulary, and agenda-setting rather than in a novel artifact or experimental result.

Abstract

This paper introduces the notion of affective extended reality (XR) to characterise XR systems that use biodata to enable understanding of emotions. The HCI literature contains many such systems, but they have not yet been mapped into a coherent whole. To address this, we conducted a scoping review of 82 papers that explore the nexus of biodata, emotions, and XR. We analyse the technologies used in these systems, the interaction techniques employed, and the methods used to evaluate their effectiveness. Through our analysis, we contribute a mapping of the current landscape of affective XR, revealing diversity in the goals for enabling emotion sharing. We demonstrate how HCI researchers have explored the design of the interaction flows in XR biofeedback systems, highlighting key design dimensions and challenges in understanding emotions. We discuss underused approaches for emotion sharing and highlight opportunities for future research on affective XR.