arXiv subjects

Mohammad Al-Ratrout

Publications and source records attributed to Mohammad Al-Ratrout.

5 recordsLinked to original sources

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.

cs.HC↗

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. Brain-based evidence can help researchers and designers understand how GILS design choices shape how learners think and process information. This scoping review follows PRISMA-ScR and synthesizes 20 empirical studies (2014-2025) in which functional near-infrared spectroscopy (fNIRS) measured brain activity during GILS use. This corpus shows that fNIRS can capture brain responses across GILS platforms and game elements, and points to how neurophysiological evidence can inform multimedia design decisions, such as that different platforms activate different brain regions, that adaptive difficulty reduces cognitive load and improves performance simultaneously, and that collaborative gameplay predicts knowledge retention. The 20 studies in this corpus reflect a field with substantial room to grow. Causal links between brain activation and learning outcomes would give designers more reliable evidence for platform decisions. As fNIRS and multimedia devices improve, standardized methods, classroom settings, and real-time neural adaptation represent directions where future work can translate these findings into practical multimedia learning systems.

cs.HC↗

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.

cs.HC↗

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.

cs.HC↗