Academa AI treats STEM lectures as source code to automate video production

PhD students Sina Atalay and Abdullah Geduk have launched Academa, a platform that reimagines STEM education by treating lecture videos as source code. The system allows educational content to be written as text and compiled into final video output, shifting the production process from manual recording to software engineering.

At the core of the platform is a "lectures-as-code" paradigm. Instead of recording a professor on camera, Academa represents lecture actions—such as speech, drawing, and labeling—as text-based code. A specialized compiler then transforms this code into a finished video using computer graphics and text-to-speech (TTS) technology. This architecture allows large language models (LLMs) to generate long-form educational videos directly. Because the content exists as code, updates can be made via simple text edits rather than expensive re-production of the entire video.

The Maintenance Problem

Traditional STEM education has long relied on static recorded videos from institutions like MIT OpenCourseWare or platforms like Khan Academy. While effective, these assets are notoriously difficult to maintain; a single outdated formula or a corrected explanation typically requires a complete re-recording of the segment. Academa applies software engineering principles, such as version control and text-based editing, to solve this maintenance bottleneck. By decoupling the content (the code) from the presentation (the rendered video), the platform ensures that educational materials can evolve as quickly as the technical subjects they cover.

Democratizing Technical Content

This shift in production has significant implications for the accessibility of specialized knowledge. By leveraging LLMs to generate the underlying code, Academa can produce high-quality visual explanations for niche technical subjects that were previously too costly or time-consuming to animate manually. Furthermore, the platform supports first-class translation into more than 80 languages. Rather than relying on dubbed audio or subtitles, the system translates the underlying code itself, regenerating the video for different linguistic markets.

The Future of Instruction

If adopted widely, this approach transforms the role of the educator from a video producer into a "code reviewer." Instead of spending hours in a studio, instructors can oversee AI-generated scripts and refine the logic before compilation. The founders envision a future where lectures are personalized in real-time to match a student's specific syllabus, though the current focus remains on the scalable generation and maintenance of high-fidelity technical content. The success of the platform will likely depend on whether AI-generated visual logic can maintain the pedagogical rigor required for advanced STEM fields.