Binghamton University has awarded four seed grants of up to $100,000 each to faculty projects that examine how artificial intelligence reshapes creative work. The latest round of Provost Awards for Research Grants funds investigations into ethical collaboration with AI, machine vision's blind spots, and whether generative systems can interpret literature - questions that matter directly to artists, musicians, filmmakers, and writers navigating a tool that's becoming impossible to ignore.
"It's really great to see Binghamton supporting this and looking toward AI in the arts and humanities. I think it's important right now for all of us, because it's so prevalent in all of our lives now," said James Budinich, a lecturer in music composition and creative sound technologies and one of the grant recipients. "We're constantly being asked to use AI, but it's important that we find ways to use AI more ethically - as more of a partner in the creative or research-based process, instead of just churning out its own research or creation."
The four funded projects span music, cinema, visual art, and literary studies, each tackling a different angle of how creative professionals can engage with AI without surrendering agency.
- "Algorithm, Composition & Improvisation: A New Generative Collaboration Via Sensory Percussion and Machine Learning" - James Budinich and Gregory Evans, assistant professor of music, received $12,530 to build a system where generative AI responds in real time to live performance data, becoming an improvisational partner for human instrumentalists. A fall workshop and performances in Binghamton, New York City, and Ithaca will follow, with guest violist Stephanie Griffin. Budinich expects no two performances to be identical. "Each time it's performed, it has a whole new liveness to it, a whole new identity. I think that is interesting, because then it may change the way we think about documentation for music, what the role of performance is, and how AI can help that," he said.
- "AI as Cinematic System" - Magdalena Bermudez and Jason Bernagozzi, assistant professors in the Cinema Department, were awarded $37,500 to address the gap between wholesale adoption and outright rejection of generative tools. They will develop a responsive machine learning system that combines historical and contemporary cinematic and audio technologies, aiming to let filmmakers retain their own artistic agency while working with AI.
- "Against Detection: Investigating Human and Machine Vision Through Print-Based Practice" - A cross-department team from Art and Design and Digital and Data Studies received $32,000. Christopher Swift, Alexandros Skouras, Ruth Carpenter, and Gregory Hallenbeck will use printmaking to create images that confuse computer vision, images that humans and machines perceive differently, and traditional artworks that remain incomprehensible to algorithms. The goal is to identify what stays "irreducibly human" in visual creation.
- "How Do Generative AIs Read Literature?: Designing a RAG Benchmark for Social Knowledge" - Junting Huang (comparative literature), Sujoy Sikdar (computing), and William Hayes (psychology) received $17,500 to curate at least 30 works of world literature and test multiple Retrieval-Augmented Generation configurations against more than 150 annotated question-answer pairs. The project probes where AI's literary interpretation holds up - and where it falls short, with direct implications for how writers and editors might use these systems.
Music as a real-time AI partner
Budinich's project leans into improvisation as a testbed for human-machine collaboration. Instead of using AI to generate finished pieces, the system will react to drum triggers and other sensory input during live performance - a technique at the heart of Generative Art. The result is a performance that resists fixed documentation, raising questions about authorship, liveness, and the archival value of a work that never repeats.
Cinema, agency, and the middle path
Bermudez and Bernagozzi's work confronts a tension familiar to many creatives: the pressure to adopt AI entirely or reject it on principle. Their responsive system is designed to offer a third option, one where the filmmaker's hand remains visible. The $37,500 grant will support development of a tool that integrates old and new technologies, placing the artist's decisions - not the algorithm's defaults - at the center of the process.
What machines can't see
The print-based project attacks a different question: are there visual artifacts that computers simply cannot parse? By generating images that slip past classification, the researchers hope to map the edge of machine perception. For visual artists and designers, that boundary is also a space of creative freedom - a zone where human intention still confounds the pattern-recognition engines that increasingly mediate how images are sorted, tagged, and valued.
Testing AI's literary interpretation
Literature has long been a vehicle for building empathy and critical thinking. Huang, Sikdar, and Hayes want to know whether generative AI systems can engage with fiction on those terms, or whether they merely pattern-match without understanding. Their benchmark, built from global texts and carefully crafted question pairs, will give writers, editors, and publishers a clearer sense of what these tools can actually do with a novel or short story - and what requires a human reader.
Why this matters for Creatives
These grants signal that universities are funding research into AI's role in the arts - not just as a productivity booster, but as a force that changes how work is made, seen, and interpreted. For anyone building a career in music, film, visual art, or writing, the findings from these 18-month projects will offer concrete evidence about where AI can act as a genuine collaborator and where it still falls short. The question is no longer whether to use AI, but how to use it without losing the human core of the work. For more on this shift, see AI for Creatives.
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