Mak Ahmad

Enterprise API design is often bottlenecked by the tension between rapid feature delivery and the rigorous maintenance of usability standards. We present an industrial case study evaluating an AI-assisted design workflow trained on API Improvement Proposals (AIPs). Through a controlled study with 16 industry experts, we compared AI-generated API specifications against human-authored ones. While quantitative results indicated AI superiority in 10 of 11 usability dimensions and an 87% reduction in authoring time, qualitative analysis revealed a paradox: experts frequently misidentified AI work as human (19% accuracy) yet described the designs as unsettlingly “perfect.” We characterize this as a “Perfection Paradox”—where hyper-consistency signals a lack of pragmatic human judgment. We discuss the implications of this perfection paradox, proposing a shift in the human designer’s role from the “drafter” of specifications to the “curator” of AI-generated patterns.

Understanding how students learn visualization skills is becoming increasingly crucial as generative AI transforms technical education. We present a systematic study examining how structured exposure to large language models via Observable’s AI Assist platform impacts data visualization education through a multiphase investigation across two universities. Our mixed-methods approach with 65 graduate students (32 data science and 33 computer science) revealed that structured generative AI exposure following constructivist learning principles enabled sustained engagement and tool adoption while maintaining pedagogical rigor. Through a structured multiphase study incorporating preassessments, intervention observations, detailed assignment reflections, and postintervention evaluation within the academic term constraints, we identified specific patterns in how students integrate generative AI into their visualization workflows. The results from our mixed-methods analysis suggest potential strategies for adapting visualization education to an AI-augmented future while preserving essential learning outcomes. We contribute practical frameworks for integrating generative AI tools into visualization curricula and evidence-based insights on scaffolding student learning with AI assistance, with initial evidence of sustained impact over a three-week period following instruction.

This study uses mixed methods to evaluate API design methods, focusing on design and consumption phases. Our goal was to understand the impact of API governance approaches on productivity and usability. A controlled developer experiment (n=34) demonstrated a 10% increased requirement fulfillment using API Improvement Proposals (AIPs) and linter versus no protocols. Meanwhile, 73% of 33 surveyed API consumers preferred AIP-aligned designs for enhanced usability and comprehensibility. Complementing this, a custom large language model called the API Architect received average expert ratings of just 5/10 for specification quality, revealing gaps versus manual design. The quantitative performance metrics combined with qualitative user feedback provide evidence from multiple angles that strategically integrating industry best practices with maturing AI capabilities can meaningfully improve API design outcomes. This research offers empirical insights from developer and consumer perspectives to advance scholarly discourse and industry practice regarding optimal API design workflows.

API Governance, the process of applying standardized sets of policies and guardrails to the design and development of APIs, has only grown in importance and prominence given the continued growth in APIs being produced. In this paper, we present an Action Research style approach to investigate and understand the utility of a multi-faceted API Governance process being adopted inside Google. We first reflect on past research around API Governance, and then introduce three new components, 1. API Improvement Proposals (AIPs) the documented source of truth for API design rules, 2. API Linter, an automated analysis tool which checks for adherence to / violations of AIPs, and 3. API Readability, a program to educate and certify API design experts. These three components are designed to build upon pre-existing processes to scale and improve API design. Through a mixed-methods research strategy, containing both a survey and a series of interviews, we evaluate the utility of these approaches in supporting API Producers. Our research shows that API Producers have positive sentiment towards API Governance, validating the general direction of the program. Specifically, our study participants highlighted the positive impact of API Governance on the quality of the APIs they produced, via consistency in both the outcome and approach. This paper also discusses future research opportunities to enhance API Governance, specifically with regards to newer API Producers, who reported worse sentiment towards the program than their more experienced peers.