AI tools can produce interface layouts, copy, and working code in seconds, but speed is not the same as quality. This topic asks a harder question: once an AI has generated something, how do you decide whether it is actually good? Design quality in an AI-assisted project still rests on the same foundations you have used all semester — usability heuristics, accessibility standards, consistency, and fitness for the intended users and context — but the review process changes because the output arrives faster, in larger volumes, and without a human author who can explain their reasoning.
The subtopics walk through a practical review pipeline. You first define what "quality" means for your project, then apply heuristic evaluation to AI-generated screens, audit generated markup and code for accessibility problems (missing labels, poor contrast, broken focus order, non-semantic elements), and establish code review habits that treat AI output as a draft from an unfamiliar collaborator rather than a finished deliverable. Finally, you consider how to keep your own design intent intact, so that the product reflects deliberate decisions rather than whatever the model happened to produce.
The three videos below approach this from complementary angles: using language models to critique UI mockups, simulating users to surface usability feedback, and the broader practice of using an AI model as an evaluator. While watching, note what each approach can reliably catch, what it misses, and where a human designer's judgment is still required. Pay particular attention to how evaluation criteria are specified — vague prompts produce vague critiques, while criteria grounded in heuristics or guidelines produce feedback you can act on.
A conference talk from the ACM CHI research community presenting a system that uses large language models to generate automatic design feedback on UI mockups. Based on the title and authorship, it likely explains how a mockup is represented for a model, how feedback is grounded in design guidelines, and how the generated critiques compare to feedback from human designers or experts. Expect discussion of both the quality of the automated feedback and its limitations.
It directly addresses heuristic-style evaluation of interface designs with AI assistance, and as peer-reviewed CHI research presented by ACM SIGCHI it models the kind of evidence-based, criteria-driven critique this topic asks you to adopt.
Another ACM SIGCHI research presentation, this one on SimUser, an approach that generates usability feedback by simulating different kinds of users interacting with a mobile application. The talk likely covers how user personas or characteristics are modeled, how simulated interaction traces are turned into usability observations, and how the results were validated. It should also surface where simulated feedback diverges from real user testing.
It extends evaluation beyond static screen critique to simulated use, which is useful when reviewing AI-generated interfaces quickly, and it prompts you to think carefully about which user differences a simulation can and cannot represent.
A short, accessible explainer from IBM Technology on the "LLM as a judge" pattern — using a language model to evaluate the outputs of another model. It is likely to cover why automated evaluation is needed at scale, how evaluation criteria and rubrics are defined, and common pitfalls such as bias in the judging model and the need for human verification. The framing is general rather than design-specific.
It gives you the conceptual vocabulary behind automated review and its reliability limits, which is exactly the caution you need when building code review and quality-check practices around AI-generated output.
Together these videos move you from understanding AI's role in the workflow toward critically judging what it produces. The two CHI talks show research-grade methods for evaluating interface designs and usability with AI support, giving you concrete models for applying heuristic and user-centered criteria to AI-generated layouts and code (MO3), while also illustrating how AI tools fit into real design and development pipelines (MO1). The IBM explainer supplies the underlying evaluation framework — defining rubrics, scaling review, and recognizing when automated judgment must be checked by a person — which supports disciplined review of the outputs you generate in your own project work (MO2, MO3).
These videos were chosen from YouTube on October 3, 2026. Each has captions; the player turns them on. Videos on YouTube can be changed or removed — if one no longer plays, tell your instructor. If a player does not appear, use its Watch on YouTube link.