Human-AI Collaboration

From Black Box to Glass Box: DeepVIS Makes Data Visualization Explain Itself

When business leaders ask for a “quick chart,” they rarely expect to become detectives in the aftermath—trying to work out why the AI picked that chart type, grouped the data that way, or left out important categories. Yet that’s exactly the frustration with most Natural Language to Visualization (NL2VIS) tools today: they generate results like a magician pulling a rabbit from a hat, with no insight into how the trick was done. ...

Truth, Beauty, Justice, and the Data Scientist’s Dilemma

As AI systems become more capable of automating every stage of the data science workflow—from formulating hypotheses to summarizing results—it might seem we’re inching toward a world where “data scientist” becomes just another automated job title. But Timpone and Yang’s new framework, presented in their paper AI, Humans, and Data Science (2025), offers a powerful antidote to this narrative: a structured way to evaluate where humans are indispensable—not by resisting automation, but by rethinking our roles within it. ...

Divide and Conquer: How LLMs Learn to Teach

Divide and Conquer: How LLMs Learn to Teach Designing effective lessons for training online tutors is no small feat. It demands pedagogical nuance, clarity, scenario realism, and learner empathy. A recent paper by Lin et al., presented at ECTEL 2025, offers a compelling answer to this challenge: use LLMs, but don’t ask too much at once. Their research reveals that breaking the task of lesson generation into smaller, well-defined parts significantly improves quality, suggesting a new collaborative model for scalable education design. ...

The Art of Control: Balancing Autonomy, Authority, and Initiative in Human-AI Co-Creation

In the expanding domain of artificial intelligence, creativity is no longer a human-only endeavor. From music composition to visual art and storytelling, AI agents are taking on increasingly creative roles. But as these systems become more proactive, one question looms large: who’s really in control? Enter MOSAAIC — a framework developed to guide the design of co-creative systems by managing autonomy, initiative, and authority in shared human-AI decision-making. The Three Pillars: Autonomy, Initiative, and Authority The authors define three interrelated yet distinct aspects of control: ...