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Alternative text (alt text) or image descriptions for scientific figures make them accessible to screen reader users.Yet, generating high-quality alt text remains a significant challenge, particularly for people lacking subject-matter expertise.Emerging AI tools, such as generative AI, can help people understand contexts where they lack subject expertise.This work explores how university Disability Services Office (DSO) professionals write alt text for scientific figures without subject expertise.We conducted a user study with 12 DSO professionals who authored alt text for scientific figures, first without and then using generative AI.We assessed the participants' processes for alt text generation and their confidence in the resulting output.We also conducted semi-structured interviews to understand DSO professionals' experiences with the alt text generation.Our findings reveal that AI assistance improved participants' confidence and efficiency, but introduced new challenges for interaction and trust.Participants expressed caution about relying on AI-generated descriptions without sufficient domain knowledge and editing.These insights highlight the need for AI-augmented tools that better scaffold the alt text writing process for accessibility professionals.
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Stored only when both papers are in the corpus — 2 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.