arXiv subjects

Roghayeh Leila Barmaki

Publications and source records attributed to Roghayeh Leila Barmaki.

At least 19 recordsLinked to original sources

arXiv2026

Neurophysiological Insights into Multimedia-based Education: A PRISMA-ScR Review of fNIRS in Game-Integrated Learning Systems

Game-integrated learning systems (GILS) are a growing form of multimedia education. Neurophysiological evidence can complement behavioral and learning measures when examining how users interact with these environments. This scoping review follows PRISMA-ScR and synthesizes 20 empirical studies published between 2014 and 2025, identified through searches updated in April 2026, in which functional near-infrared spectroscopy (fNIRS) measured task-related cortical hemodynamic responses during GILS use. The review maps learning contexts, multimedia platforms, outcome measures, and methodological approaches across the included studies. Individual studies reported differences in cortical responses across interaction conditions and associations with behavioral or learning outcomes. However, only 10 of the 20 studies assessed learning outcomes, and methodological heterogeneity limits cross-study comparisons. These findings do not establish that specific multimedia platforms cause particular neural or learning outcomes. Future research should prioritize standardized methods, ecologically valid learning settings, and evaluation of the feasibility and educational value of real-time neural adaptation.

cs.HC↗

arXiv2026

Who Is Talking to the Agent? LLMs in Multi-User 3D Virtual Environments

When several people share a 3D virtual room with an LLM agent, the agent must decide not only what to say, but whether an utterance was addressed to it and, if accessible, what profile information about the others present it may use. To study both problems, we construct LookAway, a controlled corpus of 40 sessions involving 80 distinct personas and an LLM agent (1,200 turns), including ambiguous-addressee turns in which speaker orientation agrees or conflicts with the intended addressee. Across three open-weight large language models and five conditions varying which profiles the agent sees and whether it is told where each person faces (18,000 decisions), adding speaker orientation increased addressee accuracy from 56% to 99.5% when orientation was congruent, but when the speaker faced someone other than the addressee, two of the models went by where the speaker faced on more than 85% of those turns. Warning one model that orientation could be misleading reduced this only modestly. A browser-based 3D demonstrator shows the effect live: the same sentence gets an answer when the speaker faces the agent and silence when they face the other person. Providing both personas' profiles improved responses about the person being asked about, but also increased the use of profile attributes not revealed in the shared conversation, reaching 45.3% of answers for one model. More context thus improves multi-user interaction but also leads to oversharing, so shared LLM agents need mechanisms for weighing spatial cues and controlling when user-specific information enters a response.

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arXiv2026

Reading the Eyes in VR: Multimodal Modeling of Social Intelligence

Social intelligence, the ability to interpret others' emotions, beliefs, and intentions, is often assessed with the Reading the Mind in the Eyes Test (RMET), in which participants infer mental states from images of the eye region. Yet RMET is typically presented on paper or desktop displays, where viewing geometry can vary across participants, and it rarely includes immediate feedback. We investigated whether presentation medium and brief trial-level feedback influence RMET behavior. We implemented RMET in Unity for both desktop and Virtual Reality (VR), using VR to hold stimulus distance and field of view constant without changing the items. We conducted a 2x2 mixed study with 20 participants, with device (VR vs. desktop) manipulated between subjects and feedback (immediate correctness cue vs. none) manipulated within subjects. Eye-tracking and EEG data were recorded and synchronized with behavioral logs. We analyzed fixation-based gaze measures, EEG signals, response time, accuracy, and subjective measures. Immediate feedback was associated with longer fixation durations and higher EEG-based engagement, while no significant differences were observed in task completion time or total correct answers. Presentation medium did not produce reliable differences in the objective measures, but VR received higher usability ratings and was also rated as more effortful. These results provide initial evidence that RMET can be studied as a process-aware assessment task in controlled VR and desktop settings.

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arXiv2026

The Effectiveness of Virtual Patient Simulation Versus Peer Simulation in Providing Sexual Counseling During Pregnancy: A Randomized Controlled Trial

Although sexual health counseling is one of the important responsibilities of healthcare professionals, effective educational methods are needed to develop students' counseling skills in this field. The aim of this study was to compare the effectiveness of virtual patient simulation and peer simulation methods in developing sexual counseling skills during pregnancy among nursing faculty students. This randomized controlled trial included 51 participants assigned to one of three groups: virtual patient simulation, peer simulation in a virtual environment, or face-to-face peer simulation. In the study, all groups received face-to-face theoretical instruction on sexual counseling during pregnancy. Following the theoretical training, students participated in virtual patient simulation, peer simulation in a virtual environment, or face-to-face peer simulation practices according to the groups to which they were assigned by randomization. Outcome measures included the Sexual Attitudes and Beliefs Scale, the Student Satisfaction and Self-Confidence in Learning Scale, and the Sexual Counseling Skills Evaluation Form. It was determined that the participants' SABS scores decreased significantly after the intervention. According to the results of the mixed repeated-measures ANOVA, the effect of time was statistically significant. However, the group effect and the group x time interaction were not significant. There was no difference between the groups in terms of Satisfaction in learning, Self-confidence in learning, and Skill scores. This study showed that different simulation methods used in sexual counseling education during pregnancy were effective in reducing students' negative attitudes and beliefs and provided similar educational outcomes. Therefore, virtual patient and peer simulations may be recommended as feasible approaches for improving sexual counseling skills.

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arXiv2026

Do Video-LLMs Actually Watch? Diagnosing Character-Tracking Failures in Long-Form Video

Can a Video Large Language Model (Video-LLM) follow one person through a long video, keeping track of who they are well enough to report, in order, how their outfit changes across a full TV episode? Benchmarks increasingly score this kind of task, and the strongest open-source 7--8B models now reach 37--38% on InfiniBench's global appearance task, which asks exactly that. But does that score come from tracking the named character, or from something easier? We test this with a nine-condition diagnostic protocol applied to three architecturally distinct open-source Video-LLMs, with Gemini~2.5~Flash as a frontier reference, and find the accuracy does not come from character tracking. When we change the character named in the question to a different cast member, leaving the video and answer options untouched, the models change their answer only 4--31% of the time, so they are largely ignoring who the question asks about. Breaking that test down by the gender of the swapped name shows why: the models react more when the name is changed to a different-gender character than to a same-gender one (a 13--28 point gap), picking up coarse gender cues but unable to tell same-gender individuals apart. This shallow processing surfaces again when we drop the multiple-choice options and ask the same questions open-endedly: open-source accuracy drops 18--25 points, with none of 151 answers fully correct, versus a 12-point drop for Gemini. Further checks rule out the obvious innocent explanations, adding subtitles, using the most informative frames, or doubling the number of frames all leave character tracking unimproved, so the bottleneck is not how much video the model sees but how it ties that video to the person the question names. We release a diagnostic toolkit for auditing what such benchmark scores actually measure.

cs.CV↗

arXiv2026

Indirect and Direct AI Scaffolding for Computational Problem Posing: A Pilot Experience Report

Problem posing is a valuable learning activity in computing education, encouraging learners to actively construct, refine, and reflect on problems rather than simply solving them. This experience report presents the design and pilot deployment of two LLM-powered scaffolding systems for supporting problem posing across two computational scenarios with different levels of task openness. Both systems assessed student-generated problems using Bloom's Taxonomy-based criteria and applied the same assessment framework, differing only in output modality: one provided guiding questions (Indirect scaffolding), while the other offered worked examples (Direct scaffolding). We conducted a within-subjects, counterbalanced pilot study with 20 graduate students and collected problem-quality ratings, user-experience surveys, and post-session interviews. Our deployment showed that both systems supported problem refinement in complementary ways, each offering distinct benefits. Direct scaffolding produced greater immediate improvements, while interviews showed that participants valued Indirect scaffolding for promoting deeper reflection on their own problem design. Based on these findings, we suggest sequencing the two modalities by beginning with Indirect scaffolding to promote reflection, then shifting to Direct scaffolding when learners become stuck. These lessons offer an initial practical strategy for integrating LLM-based scaffolding into computing classrooms.

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arXiv2026

When LLM Tutoring Responses Work: Evidence from Student Programming Conversations

As students increasingly use LLM tutors in computer science education, one question becomes especially important: what kind of response helps a student continue productively? Prior work has studied how students use LLMs in computer science education, but less is known about how tutoring response styles are associated with student follow-up across programming help-seeking contexts. This paper analyzes StudyChat (UMass, 2026), a public dataset of student and ChatGPT tutoring conversations from an artificial intelligence course. We transformed StudyChat into 16,851 assistant-response interactions from 203 students and 2,214 conversations. Using local LLM-assisted annotation with Gemma 4, we labeled student help-seeking situations, student state, assistant response style, and student next-turn outcome. Human validation showed 82\% agreement with the LLM-assisted labels (Cohen's $κ=.74$). We analyzed productive continuation and unresolved continuation across the full dataset and across help-seeking contexts. Globally, response style was significantly associated with productive continuation, $χ^2(7)=100.39$, $p<.001$, $V=.078$, and unresolved continuation, $χ^2(7)=125.77$, $p<.001$, $V=.087$, though effect sizes were small. Verification feedback had the highest productive-continuation rate (82.4\%), while direct answers had the lowest (62.7\%). Descriptively, response-style score ranges were smallest in low-confusion conceptual contexts (.017) and largest in high-cognitive-load contexts (.203). More detailed comparisons showed situation-dependent response patterns. For example, stepwise guidance was followed by greater confusion decrease in high-cognitive-load code requests, while direct answers were followed by more unresolved continuation in high-load debugging. These findings support context-aware evaluation and design of AI tutoring responses for programming education.

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arXiv2026

How YouTube Frames ChatGPT Use in Education: An Epistemic Network Analysis with Supporting Multimodal Metadata

We examine educational YouTube videos through multimodal metadata, such as transcripts, titles, thumbnails, and viewer comments, to investigate how ChatGPT is framed across creator groups and how those framings relate to audience response and platform reach. Little is known about how large language models are presented to learners in informal, creator-driven public discourse. Following PRISMA, we selected 52 videos for analysis. We identified three structurally distinct discourse groups: (G1) videos that positioned ChatGPT as a conceptual scaffold for thinking, (G2) videos oriented toward retrieval practice and skill-building, and (G3) videos that framed ChatGPT as a tool for output generation. Epistemic Network Analysis revealed statistically significant group differences with large effect sizes. Multimodal metadata consistently reflected these distinctions across transcript discourse, titles, and thumbnails. Viewers of learning-oriented content described ChatGPT as a thinking partner or tutor, whereas viewers of output-oriented content raised concerns about over-reliance, surface-level learning, and cognitive offloading. G3 achieved comparable platform reach to G2, yet with substantially weaker learning-oriented framing. This may suggest that output-oriented content competes for visibility despite lower pedagogical depth. These findings reveal a structural tension in self-directed AI learning: content that prioritizes quick outputs reaches far more learners than content that promotes deep engagement. This gap raises critical questions about whose vision of AI literacy scales and what learners are actually left with.

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arXiv2026

Micro-level AI Feedback Features and Student Responses in Consecutive LLM Tutoring Interactions

AI-assisted feedback research has shown that micro-level feedback features, such as concrete elaboration, affective language, and response length, are associated with learning outcomes. Existing studies have primarily examined these features using session- or task-level measures. We examine how feedback provided in one user-AI interaction is associated with student confusion and understanding in the immediately following interaction in a naturalistic tutoring setting. We focus on three micro-level features of AI feedback: concrete elaboration (analogies, comparison-based explanations, or worked examples), affective language (encouragement, empathy, or apology), and response length. We analyzed 16,851 conversational user-AI interactions from the StudyChat dataset, a naturalistic record of student interactions with an LLM tutor in an undergraduate AI course, and identified 1,718 cases in which students expressed confusion and continued to a subsequent interaction. Using chi-square tests and Generalized Estimating Equations (GEE), we found that concrete elaboration was associated with higher understanding and lower re-confusion in the student's next interaction. Empathetic language showed no significant association with either outcome, while longer responses were independently associated with lower understanding. These findings highlight the value of examining feedback across consecutive user-AI interactions and suggest that concrete elaboration may play an important role in supporting immediate student understanding.

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arXiv2026

Understanding Computing Identity Development Through Mentorship and Epistemic Network Analysis

Computing identity plays an important role in students' participation, persistence, and sense of belonging in computing, yet identity development can be difficult to capture through survey measures alone. This study examines how computing identity is expressed in open-ended survey responses from 37 participants in computing-related fields. Using a Quantitative Ethnography approach, we applied Epistemic Network Analysis (ENA) to model co-occurrence patterns among six identity-related constructs: recognition, interest, competence, sense of belonging, self-doubt, and imposter syndrome. We compared the structure of computing identity narratives between participants who reported mentorship support and those who did not. Findings showed that participants with mentorship support had stronger connections among interest, competence, recognition, and sense of belonging, suggesting a more integrated and supportive identity structure. In contrast, participants without mentorship support showed stronger connections involving self-doubt and imposter syndrome, indicating that uncertainty and feelings of not belonging were more closely connected in their narratives. A two-sample t-test comparing ENA scores showed a statistically significant difference between the two groups along the X-axis, with a large effect size (Cohen's d = 1.72). These findings suggest that mentorship is associated with differences in the structure of computing identity and may help individuals connect their interests, abilities, recognition, and belonging within computing.

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arXiv2026

AFA: Identity-Aware Memory for Preventing Persona Confusion in Multi-User Dialogue

When multiple people share a single voice assistant, the system conflates their histories: one resident's preferences can leak into another's responses, eroding utility and trust. We call this failure mode persona confusion, and we show it is a measurable problem in today's single-user dialogue systems when deployed in shared environments. We present the Adaptive Friend Agent (AFA), a modular framework that combines voice-based speaker identification with per-user memory stores to enable identity-aware, personalized dialogue across multiple users. To support training and evaluation, we construct PAT (Personalized Agent chaT), a synthetic dataset of 58,289 persona-grounded dialogue turns spanning 133 user profiles and 12 real-world scenarios. We evaluate AFA across five LLM back-ends in a standard response-quality benchmark, with a LLaMA-2-70B model fine-tuned on PAT achieving the highest overall performance. To directly measure persona confusion prevention, we introduce an interleaved multi-user evaluation protocol with a novel metric, Persona Attribution Accuracy (PAA), demonstrating that identity-aware routing improves PAA from 35.7% to 61.3%. Human evaluation confirms annotators perceive significantly higher personalization in routing-enabled responses. Our results establish that identity-aware user routing is the critical component for preventing persona confusion in multi-user conversational systems.

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arXiv2026

Beyond Cognitive Load: AI-Based Estimation of Cognitive Effort Using Brain Signals During Digital Tasks

Cognitive effort, defined as the relationship between cognitive load and task performance, provides insight into how individuals allocate mental resources during demanding tasks. This construct is particularly important in high-stakes public health and clinical training, where excessive cognitive load is associated with medical errors and burnout. This study investigates whether cognitive effort varies across task segments and whether it can be estimated at the individual level using brain signal data and machine learning. Functional near-infrared spectroscopy (fNIRS) data were collected from 16 participants performing a structured digital cognitive task consisting of four sequential segments separated by short and long rest intervals. Cognitive effort was operationalized using relative neural efficiency and relative neural involvement, integrating prefrontal hemodynamic activity with task performance. The analysis followed a two-stage approach. First, segment-level group analysis tested whether cognitive effort differed across task segments, assessing whether the task structure induced meaningful variation in cognitive demand. Second, participant-independent machine learning models were used to predict task performance from brain signal features. These predicted scores were then combined with neural measures to estimate individual-level cognitive effort. Results showed significant differences in cognitive effort across the four task segments, indicating that variations in task structure influence collective cognitive efficiency. In addition, machine learning models successfully predicted performance from fNIRS data. Cognitive effort derived from predicted scores closely matched that based on actual performance, suggesting that the proposed metric primarily reflects brain signal patterns.

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arXiv2026

Feedback Effects on Cognitive Dynamics: Network-Based Insights from EEG Patterns and Behavioral Performance

This study examines the impact of feedback on Electroencephalography (EEG) activity and performance during the Reading the Mind in the Eyes Test. In a within-subject design, eleven participants completed the test under Feedback and No-Feedback conditions. Using the principles of Epistemic Network Analysis (ENA) and Ordered Network Analysis (ONA), we extend these network-based models to explore the link between neural dynamics and task outcomes. ENA results showed that feedback is associated with stronger connections between higher frequency EEG bands (Beta and Gamma) and correct responses, while the absence of feedback activated lower frequency bands (Theta and Alpha). ONA further disclosed directional shifts toward higher frequency activity preceding correct answers in the Feedback condition, whereas the No-Feedback condition showed more self-connections in lower bands and a higher occurrence of wrong answers, suggesting less effective reasoning strategies without feedback. Both ENA and ONA revealed statistically significant differences between conditions (p = 0.01, Cohen's d > 2). This study highlights the methodological benefits of integrating EEG with ENA and ONA for network analysis, capturing both temporal and relational dynamics, as well as the practical insight that feedback can foster more effective reasoning processes and improve task performance.

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arXiv2025

Hybrid Deep Learning Model to Estimate Cognitive Effort from fNIRS Signals

This study estimates cognitive effort based on functional near-infrared spectroscopy data and performance scores using a hybrid DeepNet model. The estimation of cognitive effort enables educators to modify material to enhance learning effectiveness and student engagement. In this study, we collected oxygenated hemoglobin using functional near-infrared spectroscopy during an educational quiz game. Participants (n=16) responded to 16 questions in a Unity-based educational game, each within a 30-second response time limit. We used DeepNet models to predict the performance score from the oxygenated hemoglobin, and compared traditional machine learning and DeepNet models to determine which approach provides better accuracy in predicting performance scores. The result shows that the proposed CNN-GRU gives better performance with 73% than other models. After the prediction, we used the predicted score and the oxygenated hemoglobin to observe cognitive effort by calculating relative neural efficiency and involvement in our test cases. Our result shows that even with moderate accuracy, the predicted cognitive effort closely follow the actual trends. This findings can be helpful in designing and improving learning environments and provide valuable insights into learning materials.

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arXiv2025

Functional Near-Infrared Spectroscopy (fNIRS) Analysis of Interaction Techniques in Touchscreen-Based Educational Gaming

Educational games enhance learning experiences by integrating touchscreens, making interactions more engaging and intuitive for learners. However, the cognitive impacts of educational gameplay input modalities, such as the hand and stylus technique, are unclear. We compared the experience of using hands vs. a stylus for touchscreens while playing an educational game by analyzing oxygenated hemoglobin collected by functional Near-Infrared Spectroscopy and self-reported measures. In addition, we measured the hand vs. the stylus modalities of the task and calculated the relative neural efficiency and relative neural involvement using the mental demand and the quiz score. Our findings show that the hand condition had a significantly lower neural involvement, yet higher neural efficiency than the stylus condition. This result suggests the requirement of less cognitive effort while using the hand. Additionally, the self-reported measures show significant differences, and the results suggest that hand-based input is more intuitive, less cognitively demanding, and less frustrating. Conversely, the use of a stylus required higher cognitive effort due to the cognitive balance of controlling the pen and answering questions. These findings highlight the importance of designing educational games that allow learners to engage with the system while minimizing cognitive effort.

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arXiv2025

MENA: Multimodal Epistemic Network Analysis for Visualizing Competencies and Emotions

The need to improve geriatric care quality presents a challenge that requires insights from stakeholders. While simulated trainings can boost competencies, extracting meaningful insights from these practices to enhance simulation effectiveness remains a challenge. In this study, we introduce Multimodal Epistemic Network Analysis (MENA), a novel framework for analyzing caregiver attitudes and emotions in an Augmented Reality setting and exploring how the awareness of a virtual geriatric patient (VGP) impacts these aspects. MENA enhances the capabilities of Epistemic Network Analysis by detecting positive emotions, enabling visualization and analysis of complex relationships between caregiving competencies and emotions in dynamic caregiving practices. The framework provides visual representations that demonstrate how participants provided more supportive care and engaged more effectively in person-centered caregiving with aware VGP. This method could be applicable in any setting that depends on dynamic interpersonal interactions, as it visualizes connections between key elements using network graphs and enables the direct comparison of multiple networks, thereby broadening its implications across various fields.

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arXiv2024

From Complexity to Simplicity: Using Python Instead of PsychoPy for fNIRS Data Collection

Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical technique that measures brain activity by estimating blood oxygenation using near-infrared light. Traditionally, PsychoPy is used in many studies to send task-specific markers, requiring a separate device to interface with the fNIRS data collection system. In this work, we present a Python-based implementation to send markers directly, eliminating the need for an additional device. This approach allows researchers to run both marker transmission and fNIRS data collection on the same computer, simplifying the setup and enhancing accessibility. This streamlined solution reduces hardware requirements and makes fNIRS studies more efficient.

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arXiv2024

MMASD+: A Novel Dataset for Privacy-Preserving Behavior Analysis of Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is characterized by significant challenges in social interaction and comprehending communication signals. Recently, therapeutic interventions for ASD have increasingly utilized Deep learning powered-computer vision techniques to monitor individual progress over time. These models are trained on private, non-public datasets from the autism community, creating challenges in comparing results across different models due to privacy-preserving data-sharing issues. This work introduces MMASD+, an enhanced version of the novel open-source dataset called Multimodal ASD (MMASD). MMASD+ consists of diverse data modalities, including 3D-Skeleton, 3D Body Mesh, and Optical Flow data. It integrates the capabilities of Yolov8 and Deep SORT algorithms to distinguish between the therapist and children, addressing a significant barrier in the original dataset. Additionally, a Multimodal Transformer framework is proposed to predict 11 action types and the presence of ASD. This framework achieves an accuracy of 95.03% for predicting action types and 96.42% for predicting ASD presence, demonstrating over a 10% improvement compared to models trained on single data modalities. These findings highlight the advantages of integrating multiple data modalities within the Multimodal Transformer framework.

cs.CV↗