Temporal Constructs: Reclaiming Creative Agency in the Age of Artificial Intelligence

AI systems in creative practice increasingly replace human decision-making, leading to reduced practitioner engagement with their tools and potential cultural homogenization. The knowledge gap widens as original developers of collaborative creative technologies age out of active practice, taking institutional knowledge of human-machine collaboration with them. This knowledge includes design philosophies that prioritized human agency, technical approaches that maintained creative control, and collaborative workflows that balanced automation with artistic intention.
Contemporary AI development often ignores collaborative design principles present in legacy creative systems. Jacco Gardner's "Temporal Constructs" project addresses this gap through systematic investigation of AI-mediated collaboration between legacy systems and current practice. The project involves direct collaboration with key figures: Jeff Minter (Trip-A-Tron), David Zicarelli (Max/MSP), and Drew Schlesinger (synthesizer development).
Using Research through Design methodology, Gardner implements MIDI protocols to facilitate real-time interaction between human operators, AI interpretation systems, and legacy hardware. The resulting laboratory ecosystem positions AI as mediator rather than autonomous agent, presenting options while maintaining operational transparency. This framework requires contemporary AI systems to operate within historical technical constraints, forcing modern algorithms to work within the creative and technical boundaries that earlier generations established to preserve human creative control.
The five-year project produces documented three-way collaboration sessions, validated educational frameworks tested across multiple institutions, and open-source tools compatible with consumer hardware. Pioneer interviews create a digital archive of tacit knowledge, while performance demonstrations validate collaborative intelligence approaches through practice-based research.
Outcomes include alternatives to opaque AI systems, accessible educational frameworks, and methodologies for preserving collaborative knowledge. The research positions artistic practice as a valid AI development approach and demonstrates technological advancement compatible with sustained human creative agency.
The interdisciplinary work spans computer music research, human-computer interaction, cultural preservation, and creative technology education, establishing collaborative intelligence as an alternative to automation-focused AI paradigms.