Perspective-Aware AI in Extended Reality

Daniel Platnick; Dava J. Newman; Hossein Rahnama; Kent Larson; Marjan Alirezaie; Matti Gruener

REVIEW 3 major objections 5 minor 1 cited by

Perspective-Aware AI in Extended Reality

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read PAiR creates a closed loop in which extended-reality scenes are driven by user-specific Chronicles, identity models learned from multimodal digital footprints.

desk verdict PAiR is a clear architectural blueprint for perspective-aware XR, but the closed-loop adaptation claim is asserted, not demonstrated, so the paper should be read as a design proposal rather than a validated system. read the letter →

arxiv 2507.11479 v1 pith:ERX3ORLK submitted 2025-05-05 cs.AI cs.GRcs.HC

Daniel Platnick , Matti Gruener , Marjan Alirezaie , Kent Larson , Dava J. Newman , Hossein Rahnama This is my paper · ORCID

classification cs.AIcs.GRcs.HC

keywords Perspective-AwareAIExtendedRealityChroniclesuseridentitymodelingknowledgegraphshuman-AIinteractioninterpretablepersonalizationclosed-loopadaptivesystems

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

PAiR is a proposed framework for making extended-reality (XR) experiences respond to a user's identity, not just their immediate behavior. The paper's central claim is that a closed-loop connection between Perspective-Aware AI and XR lets immersive scenes be driven by a Chronicle—a dynamic knowledge graph built from a person's multimodal digital footprint—so the experience reflects the user's cognitive and experiential history. The authors argue this goes beyond reactive personalization, which optimizes short-term engagement, by giving the system a reasoning-ready model of who the user is. Two proof-of-concept scenarios in an XR engine show the loop in action: a financial helper that places a personalized spending chart on a specified table, and a desk environment that, sensing sadness, retrieves a happy memory and renders it as a photo frame. The value, if the framework works, is that XR can become interpretable and adaptive in ways tied to user identity rather than surface cues.

What carries the argument

The central object is the Chronicle, a dynamic knowledge graph that encodes a user's cognitive, behavioral, and experiential evolution from multimodal digital footprints. The argument's mechanism is a closed loop: a fine-tuned Reasoner LLM turns prompts and sensed states into semantic triples, queries the Chronicle, the Object Synthesizer retrieves or generates matching media, an XR Scene Generator renders it, and the User State Monitor feeds reactions back to update the Chronicle. The Situation Graph ontology supplies the semantic vocabulary that keeps the loop interpretable, while the LLM Scribe translates between human language, symbolic reasoning, and executable XR scene configurations.

What would settle it

Run two users through the same XR scene with identical prompts but deliberately different Chronicles, one with the pivotal memory removed; if the rendered scene is identical, the loop is not actually driven by the Chronicle. Separately, feed a recorded set of facial-expression frames through the Situation and Context Detectors and count how often the produced semantic triples match a human labeler's judgment; if accuracy is near chance, the emotional-cue branch of the loop has no reliable input.

Watch

Extended reading notes

Core claim

The paper's discovery is that the Chronicle, originally designed for decision support, can serve as the reasoning substrate for immersive environments. PAiR routes both explicit user requests and passively sensed emotional cues through the same pipeline: a fine-tuned Reasoner LLM converts input into semantic triples, queries the user's Chronicle, applies spatial, temporal, ontological, and thematic reasoning to anchor the result in the 3D scene, and hands the output to a synthesizer that retrieves or generates media. The rendered scene is then displayed by an XR scene generator, while the user's reactions are fed back into the Chronicle, closing the loop. The paper demonstrates this with two implemented scenarios and claims this is a foundational blueprint for perspective-aware XR rather than a finished product.

Load-bearing premise

The framework assumes a person's digital footprint can be turned into a reasoning-ready record accurate enough that the system's queries find the right memory or preference—an accuracy the paper does not measure.

Editorial extensions

If this is right

Immersive systems can move from reactive optimization of gaze, clicks, and heart rate to identity-level adaptation: the same prompt can produce different scenes for different users based on different Chronicles.
Personalization compounds over time, because user reactions are written back into the Chronicle, so each session should make the next one more aligned with the user's evolving state.
Explicit and implicit input become interchangeable: a typed request and a detected facial expression both enter the same semantic-reasoning path, so passive cues can trigger proactive experiences.
With consent, Chronicles can be shared, so XR can synthesize experiences from another person's perspective rather than only from one's own history.
The architecture is an engine-agnostic blueprint: only the XR Scene Generator needs to be adapted when moving from one XR engine to another.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper does not run this test, but if the closed-loop claim holds, an obvious evaluation is to compare scenes produced for the same prompt with a user's real Chronicle versus a shuffled or empty Chronicle; if the output does not change, the Chronicle is not doing the work.
Because the paper adds no measurement of Chronicle accuracy, a concrete next step is to benchmark whether the Reasoner's semantic triples and object retrievals match ground-truth user preferences; until then, the closed loop's benefit is an architectural promise rather than a measured effect.
The sharing of Chronicles suggests a new class of XR social applications, such as perspective-taking training, where the same scene is rendered through two different identity models and the difference between those renderings becomes the content.
A testable extension is to make the anchor-selection threshold and the emotion-to-situation inference measurable, so failures in scene grounding can be traced to either the semantic-similarity step or the detector step.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Enjoyable, clearly written framework paper—but keep expectations in check. PAiR is a plausible architectural sketch for giving XR systems an identity-level context layer via PAi's Chronicles, and the anchor-point reasoning flow in Section 4.2 is the most concrete thing here. The integration of PAi with XR is genuinely new relative to the cited literature, and the OpenDome instantiation gives a concrete sense of how the pieces fit. The paper earns credit for being explicit that the scenarios are proof-of-concept traces, not user studies.

The soft spot is structural, not cosmetic. The central selling point is a closed loop: Chronicles adapt based on evolving user behavior. That adaptation is never actually specified or evaluated. Section 3.3 mentions updating the Chronicle "when appropriate thresholds and validated theories are met" with no details, and in both scenarios the update is explicitly optional ("can optionally be used"). The stress-test note has this right: even a perfect Chronicle would not yield the claimed closed loop without a working update path. The lack of user data, metrics, and baselines is consistent with a design proposal, but then the abstract and conclusion should not say the framework "demonstrates utility." The other heavy assumption is that Chronicles—borrowed wholesale from the authors' prior PAi papers—are accurate, reasoning-ready identity models. That is plausible but unvalidated here, and self-citation alone doesn't make it so.

None of this kills the paper as a blueprint. The architecture is internally coherent, the two scenarios are illustrative rather than deceptive, and the writing is honest about the absence of empirical validation in future work. Read it as a position paper for a research agenda, not as evidence that the system works.

I'd send this to a venue that welcomes system/design proposals—a workshop, CHI late-breaking work, or an HCI venue with short-paper format—with a request to soften the utility claim and add an evaluation roadmap. It deserves serious referee engagement, so not a desk reject. I would not cite it as a validated system, but I might cite it as a representative framework for perspective-aware XR if writing a related-work section.

Referee Report

3 major / 5 minor

Summary. The paper proposes PAiR (Perspective-Aware AI in Extended Reality), a framework that integrates the authors' earlier Perspective-Aware AI (PAi) concept, based on Chronicles (dynamic knowledge graphs representing user identity), with XR environments. The manuscript describes two components (XR and PAi) with their modules (Spatial Monitor, User State Monitor, LLM Scribe, Reasoner, Object Synthesizer, Chronicle Pool), a system flow, and two scenario traces implemented in the Unity-based OpenDome engine: a financial visualization scenario and an emotion-driven desk scenario. The central claim is that PAiR creates a closed loop in which immersive experiences are driven by user-specific Chronicles that adapt based on evolving user behavior.

Significance. The paper addresses a genuine gap: current XR personalization is reactive and short-term, while identity-level, interpretable user models are rare. The architectural decomposition is clear and the scenario traces are useful as illustrations of a possible design. However, the paper provides no user data, no quantitative metrics, no comparisons, and no runnable code; the two 'proof-of-concept' scenarios are scripted examples rather than measured demonstrations. The value of the paper is therefore as a conceptual blueprint, not as an empirical validation of adaptive personalization.

major comments (3)

[Section 3.3] The closed-loop claim in the abstract and Section 1 is not supported by the described system. Section 3.3 states only that monitoring data 'can also serve as an additional source of information to update the Chronicle ... when appropriate thresholds and validated theories are met,' but no thresholds, update operations, or validation criteria are specified. In the two scenarios (Section 4.2, Step 6; Section 4.3), Chronicle updates are described as optional ('can optionally be used to update their Chronicle') and no before/after Chronicle content is shown. As written, the system is a one-shot query-and-render pipeline, and the adaptive closed-loop behavior that distinguishes PAiR from reactive personalization is not demonstrated. Please either implement and evaluate a concrete update path or explicitly re-scope the contribution to an architecture with a planned feedback mechanism.
[Section 4] The paper claims to 'demonstrate its utility' through two proof-of-concept scenarios, but the demonstration consists of hand-authored step-by-step traces. There is no evidence that the Unity/OpenDome integration was executed, that the Reasoner LLM and situation detectors produced the shown outputs, or that the rendered scenes were presented to users. No logs, screenshots from a run, timing data, or comparisons against a non-personalized baseline are provided. If the scenarios are illustrations rather than executed implementations, the utility claim should be revised accordingly; if they were executed, please provide runnable artifacts or at least detailed logged traces.
[Section 4.3] The utility of PAiR depends on the correctness of several components that are not validated in this manuscript: the off-the-shelf situation detectors mapping facial cues to semantic triples, the fine-tuned Reasoner LLM converting prompts and cues into schema-conformant queries, and the Chronicle content built by the pipeline referenced in [1]. Section 4.3 assumes, for example, that facial cues reliably produce <user, has_emotion, sad> and <situation_2, has_possible_cause, missing_someone>; a misclassification at any of these steps would yield a semantically wrong scene. Please add component-level validation or sensitivity analysis, or clearly state that these are assumed capabilities rather than demonstrated ones.

minor comments (5)

[Section 3.3] The text refers to 'Figure 3.1' but the figure is numbered Fig. 1 in the manuscript; please correct the cross-reference.
[Throughout] There are numerous formatting and typographical issues, such as 'experiences—yetcurrentsystems' missing a space and missing spaces after colons in module lists; a careful copyedit is needed.
[Section 4.2, Step 3] The semantic similarity threshold θ is introduced as a free parameter but no value or selection procedure is given; please state whether it is fixed in the implementation or how it is tuned.
[Section 3.2] The 'off-the-shelf models' used for Situation & Context Detectors are not cited; adding concrete references would make the framework more reproducible.
[Section 2.1] The Chronicle construction pipeline is summarized only by reference to [1]; a slightly more self-contained description of how Chronicles are learned and updated would help readers not familiar with the prior PAi work.

Circularity Check

1 steps flagged · score 2.0 of 10

Minor self-citation dependency; no derivation in the paper reduces to its inputs.

self citation load bearing [Section 2.1, second paragraph]
"Chronicles are constructed from multimodal digital footprints—text, images, and social interactions—collected via decentralized, privacy-preserving channels [3,19]. These inputs are embedded using modality-specific encoders and temporally segmented [1]. To semantically ground these representations, PAi uses a predefined ontology called the Situation Graph (SG), encoding high-level concepts like sentiment, emotion, location, and ambiance [6]. The complete construction pipeline is detailed in [1]."

The central premise of PAiR—that Chronicles are reasoning-ready identity models accurate enough to drive XR scene decisions—is imported wholesale from the authors' own prior publications [1,3,6] and is not independently validated or benchmarked in this paper. The PAiR demonstration therefore inherits its load-bearing user-model capability from a self-citation rather than from evidence presented here. This is a dependency rather than a full reduction, because the XR-specific steps (spatial anchoring, LLM query translation, rendering) are new content that does not collapse into the cited construction pipeline, so the circularity score is limited to 2.

full rationale

No self-definitional equation or fitted-input-called-prediction is present; the paper is an architectural proposal with hand-illustrated proof-of-concept traces. The closed-loop update claim in Section 3.3 is supported only by the sentence "This data is sent to the Reasoner to adjust the Chronicle content when appropriate thresholds and validated theories are met," with no thresholds or validated theories supplied, and Sections 4.2/4.3 make Chronicle updates optional ("can optionally be used to update their Chronicle"). That is a missing-support/completeness gap, not a circular reduction. The only circularity-adjacent issue is self-citation: the Chronicle construct, and the claim that it is reasoning-ready, rest on the authors' prior PAi papers [1,3,6] with no independent check in this manuscript. Because the PAiR integration claim adds new architecture and does not reduce to those citations, the score is 2 rather than higher.

Assumptions & free parameters 2 free parameters · 4 assumptions · 1 invented entities

The ledger shows that PAiR introduces no fitted numerical constants but relies on several unvalidated assumptions inherited from the authors' prior PAi work, plus the unspecified threshold theta and Chronicle update thresholds. The only invented entity in active use is the Chronicle, adopted from earlier papers and used here without independent validation.

free parameters (2)

Semantic similarity threshold theta for anchor selection = not specified
Section 4.2 Step 3 uses theta to filter candidate anchor points; the rendered location depends on this threshold, and no value or tuning procedure is given.
Chronicle update threshold = not specified
Section 3.3 says monitoring data updates the Chronicle when appropriate thresholds and validated theories are met; these thresholds are unspecified, so the feedback loop cannot be replicated.

assumptions (4)

domain assumption Chronicles can be constructed from multimodal digital footprints and are reasoning-ready identity models.
Sections 1 and 2.1 treat the Chronicle pipeline from reference [1] as a completed, working component; no evidence is provided in this paper.
domain assumption LLMs can translate natural language and symbolic outputs reliably into Chronicle queries and XR scripts.
The LLM Scribe Module in Section 3.2 and the scenario steps in Section 4.2 depend on accurate translation, which is not measured.
domain assumption Off-the-shelf situation and context detectors classify emotional states correctly from sensory and behavioral signals.
Section 3.2 and Section 4.3 assume facial expression and sentiment cues map to states like sad or curious without error analysis.
ad hoc to paper The Situation Graph ontology provides valid semantic grounding for Chronicle content.
The Situation Graph is introduced in the authors' prior paper [6] and assumed here as the semantic backbone; it is not a standard ontology and has no external validation.

invented entities (1)

Chronicle
purpose: Reasoning-ready user identity model that grounds personalized XR scene decisions
Chronicles are the central representation on which PAiR depends, but they originate in the authors' prior PAi papers and are not independently validated here. No external dataset or benchmark is provided.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Perspective-Aware AI in Extended Reality." pith.science (2026). https://pith.science/paper/ERX3ORLK

@misc{pith2026250711479,
  author       = {Pith},
  title        = {Pith review of: Perspective-Aware AI in Extended Reality},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ERX3ORLK}},
  note         = {Machine review of arXiv:2507.11479}
}

read the original abstract

AI-enhanced Extended Reality (XR) aims to deliver adaptive, immersive experiences-yet current systems fall short due to shallow user modeling and limited cognitive context. We introduce Perspective-Aware AI in Extended Reality (PAiR), a foundational framework for integrating Perspective-Aware AI (PAi) with XR to enable interpretable, context-aware experiences grounded in user identity. PAi is built on Chronicles: reasoning-ready identity models learned from multimodal digital footprints that capture users' cognitive and experiential evolution. PAiR employs these models in a closed-loop system linking dynamic user states with immersive environments. We present PAiR's architecture, detailing its modules and system flow, and demonstrate its utility through two proof-of-concept scenarios implemented in the Unity-based OpenDome engine. PAiR opens a new direction for human-AI interaction by embedding perspective-based identity models into immersive systems.

Figures

Figures reproduced from arXiv: 2507.11479 by the authors.

**Figure 1.** An overview of the PAiR architecture. The framework includes a PAi component and an XR component. The figure illustrates PAiR’s core modules and data flow, which transform user inputs into personalized XR experiences. The dashed line indicates data flow from user monitoring to continuously update the Chronicle. However, current AI-powered XR systems struggle to reflect deeper layers of user identity. Personalization… view at source ↗

**Figure 2.** A perspective-aware desk environment powered by Chronicle-driven semantic queries [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Share No More Than the Request Requires: Federated Disclosure for Perspective-Aware AI
cs.AI 2026-07 conditional novelty 5.0 of 10

A new protocol formalizes minimum-necessary selective disclosure over temporal knowledge graphs of personal data, but is not yet implemented.

Reference graph

Works this paper leans on

25 extracted references · 23 canonical work pages · cited by 1 Pith paper

[1]

In: Proc

Alirezaie, M., Rahnama, H., Pentland, A.: Structural learning in the design of perspective-aware ai systems using knowledge graphs. In: Proc. the AAAI- Digital Human Workshop (2024)

work page 2024
[2]

In: AAAI 2021 Symposium on Combining Machine Learning and Knowledge Engineering (2021)

Rahnama, H., Alirezaie, M., Pentland, A.: A neural-symbolic approach for user mental modeling: A step towards building exchangeable identities. In: AAAI 2021 Symposium on Combining Machine Learning and Knowledge Engineering (2021)

work page 2021
[3]

TechRxiv (2024)

Alirezaie, M., Platnick, D., Rahnama, H., Pentland, A.: Perspective-aware ai (pai) for augmenting critical decision making. TechRxiv (2024)

work page 2024
[4]

Digital Communications and Networks (2024)

Sai, S., Sharma, P., Gaur, A., Chamola, V.: Pivotal role of digital twins in the metaverse: A review. Digital Communications and Networks (2024)

work page 2024
[5]

Computers & Education147, 103778 (2020)

Radianti, J., Majchrzak, T.A., Fromm, J., Wohlgenannt, I.: A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Computers & Education147, 103778 (2020)

work page 2020
[6]

Information15(12), 766 (2024)

Platnick, D., Alirezaie, M., Rahnama, H.: Enabling perspective-aware ai with con- textual scene graph generation. Information15(12), 766 (2024)

work page 2024
[7]

ACM Comput

Alhakamy, A.: Extended reality (xr) toward building immersive solutions: The key to unlocking industry 4.0. ACM Comput. Surv.56(9) (Apr 2024)

work page 2024
[8]

International Journal of Academic and Industrial Research Innovations (03 2025)

Pasupuleti, M.K.: Next-generation extended reality (xr): A unified framework for integrating ar, vr, and ai-driven immersive technologies. International Journal of Academic and Industrial Research Innovations (03 2025)

work page 2025

Show all 25 references

[9]

In: Proc

Langley, P.: User modeling in adaptive interfaces. In: Proc. the Seventh Int. Conf. User Modeling. p. 357–370. UM ’99, Springer-Verlag, Berlin, Heidelberg (1999)

1999
[10]

Ratican, J., Hutson, J., Wright, A.: A proposed meta-reality immersive devel- opment pipeline: Generative ai models and extended reality (xr) content for the metaverse. J. Intelligent Learning Systems and Applications15(1), 24–35 (2023) 12 Daniel Platnick et al

2023
[11]

Schwarz, K., Rozumnyi, D., Bulò, S.R., Porzi, L., Kontschieder, P.: A recipe for generating 3d worlds from a single image (2025)

2025
[12]

Frontiers in Virtual Reality 3 (2022)

Germanakos, P., Sotirakou, C., Mourlas, C.I., Richir, S., Boomgaarden, H.: Ed- itorial: Immersive reality and personalized user experiences. Frontiers in Virtual Reality 3 (2022)

2022
[13]

Virtual Reality27, 1481–1505 (2023)

Adhanom, I., MacNeilage, P., Folmer, E.: Eye tracking in virtual reality: a broad review of applications and challenges. Virtual Reality27, 1481–1505 (2023)

2023
[14]

Computers in Industry 166, 104230 (2025)

Gaffinet, B., Al Haj Ali, J., Naudet, Y., Panetto, H.: Human digital twins: A sys- tematic literature review and concept disambiguation for industry 5.0. Computers in Industry 166, 104230 (2025)

2025
[15]

In: Proc

Bozkir, E., Özdel, S., Lau, K.H.C., Wang, M., Gao, H., Kasneci, E.: Embedding large language models into extended reality: Opportunities and challenges for in- clusion, engagement, and privacy. In: Proc. the 6th Conference on ACM Conver- sational User Interfaces, CUI 2024. Ass...

2024
[16]

Data and Information Management p

Jiang, T., Sun, Z., Fu, S., Lv, Y.: Human-ai interaction research agenda: A user- centered perspective. Data and Information Management p. 100078 (2024)

2024
[17]

In: Proc

Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P.N., Inkpen, K., Teevan, J., Kikin-Gil, R., Horvitz, E.: Guidelines for human-ai interaction. In: Proc. the 2019 CHI Conf. Human Factors in Computing Systems. pp. 1–13. ...

2019
[18]

van der Meulen, R., Verbrugge, R., van Duijn, M.: Towards properly implementing theory of mind in ai: An account of four misconceptions (2025), arXiv:2503.16468

2025 arXiv
[19]

Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proc. the National Academy of Sciences 110(15), 5802–5805 (2013)

2013
[20]

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer (2023), https://arxiv.org/abs/1910.10683

2023 arXiv
[21]

In: Proceedings of the 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

Graessler, I., Pohler, A.: Integration of a digital twin as human representation in a scheduling procedure of a cyber-physical production system. In: Proceedings of the 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). pp. 289–293....

2017
[22]

Multimedia Tools and Applications83, 18185–18233 (2024)

Marougkas, A., Troussas, C., Krouska, A., Sgouropoulou, C.: How personalized and effective is immersive virtual reality in education? a systematic literature review for the last decade. Multimedia Tools and Applications83, 18185–18233 (2024)

2024
[23]

International Journal of Environmental Research and Public Health19(12), 7237 (2022)

Pardini, S., Gabrielli, S., Dianti, M., Novara, C., Zucco, G.M., Mich, O., Forti, S.: The role of personalization in the user experience, preferences and engagement with virtual reality environments for relaxation. International Journal of Environmental Research and Public Hea...

2022
[24]

PLoS ONE 13(8) (2018)

Van Loon, A., Bailenson, J., Zaki, J., Bostick, J., Willer, R.: Virtual reality perspective-taking increases cognitive empathy for specific others. PLoS ONE 13(8) (2018)

2018
[25]

AI EDAM (Artificial Intelligence for Engineering Design, Analysis and Manufac- turing) 37(2), 248–265 (2023)

Tehranchi, F., Ritter, F.E.: A user model to directly compare two unmodified in- terfaces: a study of including errors and error corrections in a cognitive user model. AI EDAM (Artificial Intelligence for Engineering Design, Analysis and Manufac- turing) 37(2), 248–265 (2023)

2023

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

In: Proc

Alirezaie, M., Rahnama, H., Pentland, A.: Structural learning in the design of perspective-aware ai systems using knowledge graphs. In: Proc. the AAAI- Digital Human Workshop (2024)

work page 2024

[2] [2]

In: AAAI 2021 Symposium on Combining Machine Learning and Knowledge Engineering (2021)

Rahnama, H., Alirezaie, M., Pentland, A.: A neural-symbolic approach for user mental modeling: A step towards building exchangeable identities. In: AAAI 2021 Symposium on Combining Machine Learning and Knowledge Engineering (2021)

work page 2021

[3] [3]

TechRxiv (2024)

Alirezaie, M., Platnick, D., Rahnama, H., Pentland, A.: Perspective-aware ai (pai) for augmenting critical decision making. TechRxiv (2024)

work page 2024

[4] [4]

Digital Communications and Networks (2024)

Sai, S., Sharma, P., Gaur, A., Chamola, V.: Pivotal role of digital twins in the metaverse: A review. Digital Communications and Networks (2024)

work page 2024

[5] [5]

Computers & Education147, 103778 (2020)

Radianti, J., Majchrzak, T.A., Fromm, J., Wohlgenannt, I.: A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Computers & Education147, 103778 (2020)

work page 2020

[6] [6]

Information15(12), 766 (2024)

Platnick, D., Alirezaie, M., Rahnama, H.: Enabling perspective-aware ai with con- textual scene graph generation. Information15(12), 766 (2024)

work page 2024

[7] [7]

ACM Comput

Alhakamy, A.: Extended reality (xr) toward building immersive solutions: The key to unlocking industry 4.0. ACM Comput. Surv.56(9) (Apr 2024)

work page 2024

[8] [8]

International Journal of Academic and Industrial Research Innovations (03 2025)

Pasupuleti, M.K.: Next-generation extended reality (xr): A unified framework for integrating ar, vr, and ai-driven immersive technologies. International Journal of Academic and Industrial Research Innovations (03 2025)

work page 2025