Considering Teacher-based Research: Using Appreciative Inquiry

by Jim Parsons


My father had a saying: “Wherever you go, there you are.” Seems simple really, and perhaps more than a bit ambiguous. For him, the core teaching was you should be wary of hanging around negative people, because soon you would be as negative as they were. I have learned to buy what he was selling, not just in life but in research. As a researcher, it is easy and probably a bit provocative to focus on tearing things down – on finding the worst and sharing that worst widely. Like gossip, we seem drawn to it and it sort of pulls us in. I know researchers who have built strong careers based on hammering on things. I know some of them personally, and they don't seem that happy. As my dad said: wherever you go.

But there are other ways to research; and, more to the point, these other ways are more in line with what we hope to do within this research journal. This journal is a small attempt to help create a community of practice where teachers share what works in their classrooms – based upon research evidence – with other teachers. We have not so narrowly defined teachers’ “what works” as best practices in a narrow, non-contextualized sense, but more as a sharing of (a) what did your research help you discover that helps children learn?, (b) upon what evidence do you base your definition of successful “what works?”, and (c) what do you believe you learned that is worth sharing with other teachers so they too might improve how their children learn? With such goals in mind, I am convinced that a research philosophy/method called Appreciative Inqiury (henceforth called AI) is a valid research choice.

AI is a choice of focusing, as researchers, on strengths rather than on weaknesses. It is also a way to bridge the goals of research more closely to the goals that, I believe, we should work to establish within our schools and classrooms. In short, teachers do well to “appreciate” and build upon the strengths they see in their students, the curriculum, and the pedagogical and instructional choices they make.

As a research methodology, the basic principle of AI is affirmation, and its basic goal is to negotiate solutions. Specifically, AI (a) helps to discover elements and factors in any group that have enabled success and (b) build upon those elements and factors improve the future. AI helps to create a positive vision of a personal or corporate future.

AI contrasts to a more analytic research methodology that focuses upon solving problems. Problem-solving research identifies problems, analyzes their causes, and then seeks treatments. According to such research, philosophically, people and groups have problems that must be fixed. Alternatively, AI contrasts with deficit-based research methodologies and is based upon research assumptions that recognize and value the best in people and their possibilities; it affirms human strengths and successes; and it shows that humans can impact and shape the systems in which they live in the ways they most value. AI assumes (as a research assumption) that people, groups, and organizations (in our case, schools and teachers) can engage their own solutions. It asks three questions: (a) What do we value? (How do we understand the best of what exists?), (b) What might become? (How can we work together to better understand what could and should be?), and (c) How should we innovate? (How should we move towards what might become?)

As a way of engaging people in research, AI centrally involves mobilizing inquiry (research) by asking unconditional positive questions that strengthen a person’s or group’s capacity to move towards positive change, thus helping people envision and create practical futures. Fundamentally, AI unites people in dialogue to talk about past and present capacities, successes, ideas, potentials, strengths, opportunities, lived values, traditions, stories, insights, and future visions; it engages people in world-building discourse.

Similarly to formative assessment, in which students’ (or participants’) answers guide teacher (researcher) inquiry in ways that help gather evidences of learning, the conversations of AI are nonlinear; however, these conversations shape a considered path. Because AI avoids deficit-based vocabularies, it organizes human thinking towards efficacy in change. AI is based on the heliotropic principle (things respond to sunlight) and helps people and groups discover the most effective and constructive ways to envision and create preferred futures. Instead of beginning with problems to be solved, according to AI, action towards change is socially created.

As a research methodology, AI utilizes (a) discovering (conversing with others to identify strengths), (b) dreaming (conversing about what might be if peak moments were the norm), (c) designing (developing “provocative propositions” to achieve one’s visions and strategies to implement them), and (d) delivering (acting upon provocative propositions, establishing new relationships, and mobilizing resources). As Cooperrider and Whitney (2005) defined it, an assumption in AI is that living systems have many “untapped, rich, and inspiring accounts of the positive” (p. 18). Although much of the AI literature is based in the area of organizational change and, admittedly, sounds rather expressive for educational research, I believe its philosophy translates well to what our goals as classroom teachers and school researchers might be.

Orr and Cleveland-Innes (2015) noted that AI is based upon five main principles that reflect its theoretical base and views on change: (a) constructivist principle, (b) principle of simultaneity, (c) poetic principle, (d) anticipatory principle, and (e) positive principle.

Constructivist principle. According to the constructivist principle, groups are living, human constructions that are ever-changing and growing. Thus, research involves more than just collecting information: it involves building possible futures.

Principle of simultaneity. In the principle of simultaneity, research inquiry is an intervention. In AI, the research questions a researcher asks and the changes conversed about are not separate but, rather, simultaneous events. In AI, change is embedded in the questions asked, and the conversations that result from engaging research questions become part of how participants might shape their work. Thus, positive and appreciative guiding questions serve as implicit encouragement to work towards positive futures.

Poetic principle. The poetic principle says that groups are emerging stories, constantly co-authored and reinterpreted. Thus, human experience is open to exploration and consideration. AI makes the unfolding story explicit. Participants and reserchers work together to author their stories in concert with others with whom they share experiences.

Anticipatory principle. According to the anticipatory principle, humans anticipate new futures. Thus, research conversations about the past also guide future behavior. During research, the kinds of questions asked also shape participants’ future behaviors.

Positive principle. In AI, researchers encourage positive conversations and imagery that lead to new, even multiple, futures. Specifically, because conversation is foundational to AI, research best engages in communal forums that involve all participants. This action leads to AI’s final principle, the positive principle. Because AI is ultimately relational, positive conversations focus on shared meanings and encouraging change. As Orr and Cleveland-Innes (2015) noted, the more positive the central driving question is, the more momentum for change is created, and the longer the change will last.

I encourage teachers who are beginning to do research to consider AI as a philsophical research choice. One piece of advice I often give new researchers is to build a research study they can live with. By that, I mean that a research study will be something a researcher engages for a long while; so, it is wise to choose something you can live with for a long while. Sounds as ambiguous as my father’s advice, I know. But the concomitant activities of research  - those things that come along with any research study – are the joy and pleasure (or conversely the drudgery) one engages as one researchs. And, my research experience suggests that choosing an appreciative topic over a negative topic keeps you going when life conspires and when work grows more difficult. Finally, for anyone more interested in engaging Appreciative Inquiry (AI), I encourage the two readings listed below.



Cooperrider, D., & Whitney, D. (2005). Appreciative inquiry: A positive revolution in change. San Fransisco, CA: Berrett-Koehler.

Orr, T., & Cleveland-Innes, M. (2015). Appreciative leadership: Supporting education innovation. International Review of Research in Open and Distributed Learning, 16(4), 235-240.