2016 Main Conference Talk
How can we tell good IA from bad IA? What can we use to evaluate our work and persuade coworkers and clients that it’s good – or to pivot if it’s bad? More than this, what tools will help us understand how well we’re practicing and teaching IA overall, and help us develop IA as a discipline?
To answer these questions, IA needs a framework for addressing how we think about and improve our field at multiple levels. We need a language of critique.
We criticize wireframes every day. But on what basis? Do we also scrutinize the theories that inform how we solve IA problems? And do we consider epistemology, the realm of meaning above that, and take responsibility for paradigms that let us theorize and build our practical IAs? Well, even if we tried once or twice, we don’t do it seriously enough.
This talk will introduce audience members to a movement currently underway to develop a robust framework for IA critique. In a nutshell, it will explain the Reframe IA model. The main takeaway is the ability to differentiate between big-thinking paradigms (epistemology), theories and models (science), and day-to-day work practice (solutions to problems), and to understand why the distinction is important to IA’s practice, teaching, and future. Finally, it will share avenues to further explore and participate in shaping a language of critique for IA.
Participants will be able to de-conflate questions of IA goodness or badness and distinguish among 3 key levels of critique: day-to-day practices, IA/UX theories and models, and the paradigms that shape what we can do or can even think about doing. For those individuals who are already engaged in elevating the practice (e.g. by teaching, mentoring, speaking, conducting research, publishing), the talk aims to spark thinking about next steps in raising IA as a discipline. I will also share avenues to further explore and participate in shaping a language of critique for IA.