As virtual 3D environments become more prevalent, equitable access is essential for blind and low-vision (BLV) users, who face challenges with spatial awareness, navigation, and interaction. Prior work has explored supplementing visual information with auditory or haptic modalities, but these methods are static and offer limited support for dynamic, in-context adaptation. Recent advances in generative AI allow users to query and modify 3D scenes via natural language, introducing a paradigm that offers greater flexibility and control for accessibility. We present RAVEN, a system that enables BLV users to issue queries and modification prompts to improve the runtime accessibility of 3D virtual scenes. We evaluated RAVEN with eight BLV people and six Unity developers, generating empirical insights into how conversational programming can support personalized accessibility in 3D environments. Our work highlights both the promise of natural language interaction—intuitive, flexible, and empowering—and the challenges of ensuring reliability, transparency, and trust in generative AI–driven accessibility systems.

Despite growing interest in using LLMs to generate feedback on students’ writing, little is known about how students respond to AI-mediated versus human-provided feedback. We address this gap through a randomized controlled trial in a large introductory economics course (N=354), where we introduce and deploy FeedbackWriter—a system that generates AI suggestions to teaching assistants (TAs) while they provide feedback on students’ knowledge-intensive essays. TAs have the full capacity to adopt, edit, or dismiss the suggestions. Students were randomly assigned to receive either handwritten feedback from TAs (baseline) or AI-mediated feedback where TAs received suggestions from FeedbackWriter. Students revise their drafts based on the feedback, which is further graded. In total, 1,366 essays were graded using the system. We found that students receiving AI-mediated feedback produced significantly higher-quality revisions, with gains increasing as TAs adopted more AI suggestions. TAs found the AI suggestions useful for spotting gaps and clarifying rubrics.

Open-ended writing assignments are central to higher education, yet heterogeneous submissions and scale make evaluation difficult. Automated writing evaluation (AWE) promises speed but often trades away transparency and sidelines human judgment. This paper repositions the AI as an on-demand collaborator that can provide specific, targeted support. In a formative study, we expose leverage points in three cognitive dimensions: evidence identification, comparative judgment, and feedback composition. Guided by these insights, we build EvaluAId, which supports interactive rubric-content mapping, adaptive benchmarking and self-calibration, and personalized, rubric-aligned feedback synthesis. Through a within-subjects study with 12 TAs, we evaluate how this approach supports grading compared with a rubric+LLM chatbot and an LLM-based AWE; EvaluAId improved alignment with expert ratings and increased graders’ satisfaction. Finally, interviews with TAs, instructors, and students underscored the value of thoughtfulness supported by EvaluAId while surfacing practical considerations for integration into classroom. Together, our results argue for deliberate, evidence-first, human-in-the-loop evaluation.

Figure 1: RAVEN is an interactive system that empowers BLV users to query and modify 3D scenes via natural language.The above image illustrates an example of an accessibility modification: A) A low-vision user types in a modification text.B) The system integrates runtime code generation LLM agent with dynamic scene information and instructions to apply accessibilityenhancing changes at runtime.C) The system compiles LLM-produced code to achieve modification while providing spoken response to the user.