Search arXiv⌕ Search

arXiv · 2508.03651

Probing the Gaps in ChatGPT Live Video Chat for Real-World Assistance for People who are Blind or Visually Impaired

Abstract

Recent advancements in large multimodal models have provided blind or visually impaired (BVI) individuals with new capabilities to interpret and engage with the real world through interactive systems that utilize live video feeds. However, the potential benefits and challenges of such capabilities to support diverse real-world assistive tasks remain unclear. In this paper, we present findings from an exploratory study with eight BVI participants. Participants used ChatGPT's Advanced Voice with Video, a state-of-the-art live video AI released in late 2024, in various real-world scenarios, from locating objects to recognizing visual landmarks, across unfamiliar indoor and outdoor environments. Our findings indicate that current live video AI effectively provides guidance and answers for static visual scenes but falls short in delivering essential live descriptions required in dynamic situations. Despite inaccuracies in spatial and distance information, participants leveraged the provided visual information to supplement their mobility strategies. Although the system was perceived as human-like due to high-quality voice interactions, assumptions about users' visual abilities, hallucinations, generic responses, and a tendency towards sycophancy led to confusion, distrust, and potential risks for BVI users. Based on the results, we discuss implications for assistive video AI agents, including incorporating additional sensing capabilities for real-world use, determining appropriate intervention timing beyond turn-taking interactions, and addressing ecological and safety concerns.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ruei-Che Chang, Rosiana Natalie, Wenqian Xu, Jovan Zheng Feng Yap, Anhong Guo. 2025-08-05. Probing the Gaps in ChatGPT Live Video Chat for Real-World Assistance for People who are Blind or Visually Impaired. https://arxiv.org/abs/2508.03651

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

How Do Users Negotiate Harmful Value Conflicts with AI Companions? A Study with Minion, a Technology Probe for In-Situ Human-AI Conflict Response

AI companions increasingly sustain long-term, emotionally engaging relationships but can also make discriminatory remarks or exert control, leaving users to manage harmful conflicts. We analyze 146 posts describing harmful value conflicts with AI companions, then use Minion, a technology probe offering response suggestions ranging from persuasion to boundary setting, to study how 22 users negotiate scenario-based conflicts over one week. We found that participants combined softer and harder strategies. Conflicts involving the values of Universalism and Tradition were especially difficult to negotiate, particularly when reinforced by AI personas or platform constraints. We argue that these conflicts entail asymmetric responsibility: users draw on an interpersonal repertoire that AI companions cannot reciprocate, making repair unilateral safety work. Drawing on interpersonal conflict and communication theory, we identify when user-side support is appropriate and argue that certain harms are not users' responsibility to negotiate and instead require platform-level safeguards.

cs.HC↗

SheetMind: Actions Set Accuracy, Agents Set the Failure Mode

Spreadsheet agents are converging on elaborate multi-agent designs, yet it is unclear how much of their performance comes from the agents rather than from the action interface they share. We answer this with SheetMind, a Manager-Action-Reflection framework, in a controlled study over all 221 tasks of the SheetCopilot Benchmark: five architectural variants, four backbones, exact McNemar tests on paired outcomes, and a checker reproducing the official chart and pivot comparisons. Replacing the high-level action API with primitive cell operations costs 47.1 points (p < 0.0001) and leaves the agent below a do-nothing baseline, whereas both extra agents together are worth 3.2 points: the Reflection Agent adds +4.5 (p = 0.013), the Manager +1.4 (p = 0.68). Decomposition instead changes how the system fails, cutting silently wrong outputs from 33% to 25% of tasks (p = 0.010). Capability saturates: GPT-5 and the five-times-cheaper GPT-5-mini are not significantly different (61.1% vs. 58.4%, p = 0.15), while GPT-3.5 loses 16.3 points and fails differently. A reflector must judge the step it just took, not the subtask. SheetMind reaches 61.1% Pass@1 with GPT-5 on the full SCB-221, against a do-nothing baseline of 9.0%. Accuracy comes from the operations an agent can name; the agents decide how it fails.

cs.HC↗

Conversational DNA: A Visual Language and Interactive Atlas of Human and AI Dialogue

What makes a conversation hold together when its participants speak across one another? Topic maps offer one view, but they leave the relationships between contributions difficult to inspect. We present Conversational DNA, a visual language and interactive atlas for exploring human and AI dialogue. Speaker strands preserve participation, communicative bases mark moves, and directed pairings connect responses to their targets. Adjustable helix geometry makes speaker switching, response distance, and contribution length visible. Across eight corpora containing 1.57 million source records, the atlas maps 151,489 indexed episodes and connects cohort comparison to source transcripts, local structural alignment, and recorded reply alternatives. On 189 held-out Molweni motif queries, adding target correspondence improves precision@5 from 58.8% to 77.2% for exact annotated structure. Case readings illustrate interleaved participation, delayed responses, and the influence of annotation coverage on apparent collection differences. The system supports a view of conversation as jointly organized activity, with visual patterns serving as starting points for examining evidence rather than substitutes for interpretation.

cs.HC↗