11 Appreciative Inquiry in RRU Mid-Career Student Life

Niels Agger-Gupta
Associate Professor
School of Leadership Studies
Royal Roads University

Ann Perodeau
Associate Faculty
School of Business
Royal Roads University

Abstract

The MA-Leadership (MAL) Program features an adult learning model and highlights three elements of the RRU Learning and Teaching Model (LTM): “facilitate authentic, challenging, collaborative and engaging learning experiences”; “focus on applied research-informed learning”; and “create learning conditions that are respectful, welcoming and inclusive.” How these LTM elements have worked for Royal Roads University students in the context of a relatively new approach to organizational change—Appreciative Inquiry—is presented and recommended as an effective approach that strongly supports the LTM.

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Introduction

Since the first cohort of the Master of Arts in Leadership (MAL) program opened at Royal Roads University in 1996, our students, mid-career professionals with a minimum of five years of leadership experience in hand, have been leading a final-year inquiry-based change process in a real world organization as their capstone project. These action research projects have taken place in organizations in every sector, including business, education, government at the municipal, provincial, and federal levels, policing and the military, healthcare, and non-profit social services agencies. The project requires the student to engage a senior organizational leader as a project sponsor, while working with an academic supervisor from RRU. With the Sponsor and Supervisor, the student creates a collaborative action research inquiry topic that helps organizational stakeholders come to some agreements and new directions on an opportunity or innovative change, leading to new knowledge for the organization or the field. Topics have ranged from improving leadership training, redesigning health systems, LEAN implementations, succession planning, employee engagement, municipal re-development, and program planning, to name only a few. The student’s task is to meaningfully—and ethically—engage the stakeholders about the leadership issue or opportunity, create opportunities for dialogue, facilitate new and generative collective understandings, and foster generative group decision-making on next steps (Rowe, Piggot-Irvine, Graf, Agger-Gupta, & Harris, 2013).

The MAL Program and Four Principles

The MAL program consists of two years of interdisciplinary study where students focus on relevant, real world opportunities to address leadership issues and challenges through projects grounded in a rigorous theoretical understanding of the nature of organizations, organizational change, and the changing role of leadership in the contemporary contexts of volatility, uncertainty, complexity, and ambiguity (or VUCA—see Johansen, 2012). The program is competency-based and focuses on adult learning, individual values and culture, inter-personal and group communication, systems thinking, team facilitation and development, and leading organizational change, and then puts these together through the capstone project with a real world organization. The School of Leadership Studies developed a set of four principles to help frame the school’s understanding of leadership, including “leadership as engagement,” an “orientation to possibility,” “engaged scholarship,” and “learning as transformation” (Harris & Agger-Gupta, 2015). An orientation to possibility “helps us to focus on the bigger issues of meaning and hope while being pragmatic about the resources, materials, and processes that will help us and our colleagues move toward a desirable future that has not yet been determined” (p. 6). Leadership as engagement is about fostering the aspirations, skills, and talents of others, and creating opportunities for organizational stakeholders to take ownership of organizational innovation and collaborative work toward common purposes (p. 2). Engaged scholarship is about creating opportunities for co-creating new knowledge for social benefit by promoting a “synergistic and dialectical relationship among scholars, practitioners and stakeholders” (p. 4). Learning as transformation is about “learning how to learn (second-order learning) and developing [one’s own] understandings and values about…relat[ing] to the world (third-order learning)” (p. 7). Fostering transformative learning means going “beyond teaching knowledge and application of skills to creating a learning environment and activities in which students learn to transform themselves and society,” along the lines of the UNESCO learning goals (2008, p. 8).

The Insight of Appreciative Inquiry

The framing of “leadership as engagement” is fully in accord with a fundamental leadership question asked in an appreciative process: “What is the behaviour that we want to grow?” and not “What is the behaviour that we want to stop?” (Lewis et al., 2011., p. 24). The critical element in an Appreciative Inquiry (AI) process is therefore about exploring possibilities for appreciation and what could be done rather than a deficit-focused repair of something gone wrong. This is not to say that one should not engage in critical path analysis or other inquiries to learn from mistakes—simply, that there are many organizational improvement processes beyond “fixing problems.” The key insight of AI is that innovation and creativity come from what Adams (2009) calls a “learner paradigm,” a generative and transformative “orientation to possibility” that is about continuous, joyful curiosity and learning from the environment, and then “tracking and fanning” the resulting sparks of innovation into being:

Tracking is a state of mind where one is constantly looking for what one wants more of. It begins with the assumption that whatever one wants more of already exists, even if in small amounts. Fanning is any action that amplifies, encourages, and helps you to get more of whatever you are looking for (Bushe, 2005, p. 127).

At the opening of RRU in 1996, AI (Cooperrider & Srivastva, 1987; Bushe, 2012) was a relatively new concept in the organizational change literature. Yet even in the first cohort of the MAL program (then the MA Leadership and Training program), one student conducted an AI process into improving leadership skills in public health nurses in two BC health authorities as her capstone (Buckingham, 1998). AI is an innovation in organizational inquiry because it links a generative, affirmative organizational inquiry process to an epistemology of social construction (Gergen & Gergen, 2015; Rosenau, 2001; Thatchenkery & Chowdhry, 2007, Yu & Sun, 2012), and dialogic change (Bushe & Marshak, 2014; 2015). AI creates organizational energy and alignment through discovering and appreciating the stories employees and other stakeholders tell about their successes. The telling of these narratives of previous success and inspiration are the evidence, and sparks, of the positive energy that is the basis for a cohesive change process. The opportunity for this storytelling happens through individual interviews or group approaches to gathering narratives from organizational stakeholders. These stories also reveal the core values that participants believe was a source of the success, as well as the hopes stakeholders have for the organization’s future (Cooperrider and Srivastva, 1987; Bushe, 1999; Bushe, 2012; Barrett and Cooperrider, 2001; Watkins et al., 2001, 2011; Lewis et al., 2011).

Two Challenges for the MAL Capstone

Two issues have challenged the school in sharing our excitement about the work our students are doing in their capstones with AI that really exemplify the RRU Learning and Teaching Model: 1) a question about whether our capstones are acceptable as Action Research (AR) projects, and 2) a question about whether a capstone using the processes of AI is actually an AR project. An explanation of the response to these questions will explain a number of relevant aspects of both AI and AR.

Given the nature of the MAL capstone as an action research process, it has been a challenge to demonstrate that the capstone projects actually are, in fact, action research projects, since structural change outcomes frequently take place after the capstone projects are concluded. Unless one is an autocratic leader, organizational change requires a cycle of action research about aligning to a common organizational change objective, as well as engaging organizational stakeholders on the topic of readiness for change. This additional aspect of the organizational AR process seemed to be a gap in the AR literature that spurred on our description of an “action research engagement” cycle (Rowe et al., 2013). It is also the issue that moves the organizational AR process beyond the personal change described in much of the AR literature and, from the standpoint of the time available for a meaningful master’s project, makes this achievable in an academic context.

A second challenge in our program has been to demonstrate that AI, which is more than a single method, actually fits under the mantle of AR, given that AI can appear to have a different epistemology and orientation, and, to some, has been construed as a different methodology. The original writings of AI founder, Cooperrider, and others (see Cooperrider & Srivastva, 1987; Bushe, 2012) accentuated this issue, since they critiqued AR as having abandoned generative, emergent outcomes and theory building usually associated with research and inquiry, in favour of defining AR as problem-solving. Only recently has it become clearer that, yes, AI is a related approach under the broad umbrella of action research (Bradbury, 2015; Cooperrider, 2013). It is in support of this larger perspective that it becomes helpful to describe how the phases of AI relate to the phases in the AR cycle.

AI Phases Linked to the AR Cycle

An Appreciative Inquiry (AI) process typically cycles through four or five phases, described as the 4-D or 5-D model: “Define,” “Discover,” “Dream,” “Design,” and “Destiny” (Watkins et al., 2001, 2011; Barrett & Cooperrider, 2001). These AI phases parallel fairly closely the action research cycle of “Plan,” “Look,” “Think,” “Act,” and “Reflect” (Stringer, 2007; Coghlan & Brannick, 2010) (see Figure 1).

The “Plan” phase of the AR cycle corresponds closely to the “Define” phase in AI, added to Cooperrider’s 4-D model by Watkins et al. (2011), since part of the planning for an AI initiative requires the organizational sponsor to be in agreement with the project as focused on the positive.

The “Discover” phase involves appreciating the narratives told by organizational stakeholders about their most inspirational moments in the organization. These stories describe the “life-giving forces” in an organization’s existence. In some versions of AI, there is a search for the “positive core” of the organization or team. These stories, together with the hopes participants express for the future of the organization, are the “data” that move the AI process forward into the “Dream” phase.

Figure 1. AR Cycle and AI Phases.
Figure 1. AR Cycle and AI Phases.

The equivalent AR element closest to the AI “Discover” phase is the “Look” phase, typically described as data gathering, or “reconnaissance” in much of the AR literature (as, for example, in Stringer, 2007). Gergen & Gergen (2015) describe this interpretation of the “Look” phase of AR as belonging to an instrumental, diagnostic approach to change. They posit that AR should have a more emergent framing based on inquiry, that harkens more closely to the scholar-practitioner intent of AR founder, Kurt Lewin. They describe the potential for AR to be a dialogic, socially-constructed way of “replacing methodological individualism with a collaborative epistemology, moving from a vision of research as mapping to one of world making” (p. 401). This conceptual “world making” in AI is the “Dream” phase, involving an iterative, participant-engaged, interactive data analysis, linking the stories, hopes, and values of the organization at its best to the creation of statements of possible futures, also known as “provocative propositions,” that are based on the “Discovery” narrative data. In AR, the equivalent is the “Think” stage of data analysis (see for example, Saldana, 2013).

In AR, the analysis of the “Think” stage leads to an “Act” stage, where some change action takes place amongst the organizational stakeholders. Some action researchers (see for example, Stringer, 2007; or, Kemmis & McTaggart, 1988) posit “action” in AR as requiring a “thoughtful variation of practice…toward improvement…that is observed” (Kemmis & McTaggart, 1988, p. 12). A social construction perspective of “action” would see dialogue as meaning-making, transformative learning, and the shifting of mental models. This is the critical precursor to any subsequent structural change (Gergen & Gergen, 2015). In the MA-Leadership program, for example, we argue that AR is not only iterative, but holographic, in that embedded within each phase of AR is an iteration of the full AR cycle. Therefore, aligning AR with a social construction perspective means that the MAL capstone involves the engagement of organizational stakeholders in the first or second of multiple loops around the AR cycle. These conversations and dialogue are what we have described as “action research engagement” (Rowe et al., 2013).

The AR “Act” phase therefore can typically also involve a meeting with key organizational stakeholders, including the organizational sponsor, that we in the School have dubbed the “Make-It-Happen” meeting. The findings from earlier stages in the process are presented and the stakeholders swim with their data, come to some collective conclusions and agreements, and plan next-step strategies to move forward. In AI parlance, this would be the “Design” phase with an “AI Summit” of stakeholders coming together to hear about earlier phases and engage in a large group activity to design their next steps. AI Summits (and “Make-It-Happen” meetings) are opportunities to bring “the whole system into the room,” a concept that originated with Emery and Trist (Emery & Trist, 1965; Dean, 2001; Cooperrider & Whitney, 2005; Cooperrider, 2012; Weisbord, 2011). At the summit, the findings and “preferred futures” statements are validated and extended, and next-step strategies are collaboratively developed with the organizational sponsors in the room (Ludema et al., 2003; Watkins et al., 2011). This AI Summit event has also frequently been structured by students as a World Café (Brown & Isaacs, 2005) or other large group activity (Bunker & Alban, 2006) in many capstone projects.

The last phase in the AI process is the implementation of the plans from the Design phase and was called “Destiny” by Cooperrider and Whitney (2005). This equates to a blending of the last part of the “Act” and “Reflect” stages in the AR cycle. This could simply be the real-world roll-out of the plans and commitments made in the “Design” summit. Most Leadership students are able to at least plan the “Design” phase summit as part of their capstone, but it is the rare project with an AI “stance” that is able to move to the “Destiny” phase. Since our program only has time for the engagement cycle, the conclusion of the student’s AR process typically results in a “Reflect” or “Reconstruct and Recontextualize” stage with the Sponsor (Coghlan & Brannick, 2010). The key questions for subsequent iterations of the AI phases or the AR cycle are, “what did this action process mean for stakeholders?” and “what should be done next?” The AI (and AR) process then cycle around to the next spiral.

The AI Metaphor

AI challenges the traditional notions of academic study of organizations and organizational issues as problems or deficits. AI reconceptualizes the organization as a learning brain, or a network, instead of as a machine in need of “fixing” (Morgan, 2006). The alternate metaphor Cooperrider describes for AI is that of the organization as a mystery to be understood, rather than as a problem to be solved (Bushe, 2015). In this reconceptualization, successful organizational inquiry leading to generative change came about through discovering the stories of joy and inspiration in the organization, and generated innovation, excellence, and community-building (Cooperrider, 2012; Cooperrider, 2013; Lewis et al., 2001; Watkins et al., 2011). AI creates generative organizational energy and alignment through an appreciation of the organization at its best (Cooperrider & Srivastva, 1987; Bushe, 1999; Barrett & Cooperrider, 2001; Watkins et al., 2001, 2011; Lewis et al., 2011; Bushe, 2012).

Despite the epistemological challenge from AI over the last 30 years, “fixing problems” is still the dominant mental model within the business sector, as well as within the world of action research and traditional academia. But this has not generally been our experience with AI at RRU. The stories of just a few of the RRU student AI initiatives illustrate how they have implemented this approach; they demonstrate why AI is an attractive approach for RRU students in their assignments, workplace applications, and projects, and why AI, as per the LTM, “facilitates authentic, challenging, collaborative and engaging learning experiences,” while “focusing on applied research-informed learning” and “creating learning conditions that are respectful, welcoming and inclusive.”

The RRU Experience with AI Capstones

Between 1998 and 2015, there were 114 accepted RRU master’s capstone reports listed in the UMI/ProQuest database that included the phrase “Appreciative Inquiry” in either the title or abstract. Of the 19 capstones with “Appreciative Inquiry” in the title of the study, the MAL program has sponsored 15, while three other RRU programs included one each: MA in Health Leadership, MA in Environmental Education and Communication, and the MA in Professional Communication (Buckingham, 1998; Johnson, 1999; Hoffman, 2001; Fenwick, 2002; Magee, 2002; Bonney, 2003; Troje, 2003; Camara, 2005; Laing, 2005; Farr, 2006; Maber, 2006; MacDonald, 2006; Klassen, 2009; Lawrie, 2009; Waddell, 2010; Humer, 2012). Since 2010, students engaging in an AI project under the umbrella of the MAL action research methodology have not included this language in the title of their capstone and refer to their process as having an “appreciative stance.”

In a comparison of the findings and recommendations across these 19 capstone projects, several strong findings stood out in almost all of these student reports:

  • “Appreciative Inquiry helped the organizational participants to build stronger relationships”;
  • “The AI process was a positive experience for participants and for their organizations”;
  • “AI generates group energy, and contributes to employee engagement”;
  • “Organizational leaders, through their organization’s AI process, found that their role in their organizations, over the course of the initiative, had changes away from managerial logistics to the task of building a learning organization”;
  • “Through the AI-focused capstone project, it became clear that AI was more than a competing organization theory—an appreciative, generative lens becomes a way of living for members of the organization—and for the student engaged in this process”;
  • “The appreciative interview in the Discover phase of AI can have a therapeutic effect on participants”; and
  • “Students engaged in an AI process in a health organization found the AI process to be appropriate for Health Care System Research.”

In the most frequent four of the 11 kinds of activities in these 19 AI capstone initiatives, students reported conducting appreciative interviews where they collected stakeholder narratives of inspirational organizational events (15 of 19); worked with an advisory team to facilitate a whole organization focus (14 of 19); conducted appreciative focus groups where six to nine participants shared their stories and hopes with one another (10 of 19); conducted a survey of organizational stakeholders (9 of 18); and created an AI Summit or event (10 of 19) for their organizations. These AI Summits were handled in a variety of ways and included participants breaking off in pairs and triads to conduct appreciative “discovery” interviews with one another. Participants were then asked to report back the key elements of what they had heard, which then led to further rounds of interaction in the summit groups. In one case, a student had a group re-envisioning community health care in Kelowna, BC address all four AI phases (Discover, Dream, Design, and Destiny) in the course of an AI Summit taking place over one afternoon and evening (Humer, 2012).

Burnaby Family Institute Example

Katalin Camara, an employee of the Burnaby Family Institute (BFLI), chose her own organization for her final thesis, Freedom to be Positive: Implementing Appreciative Inquiry at Burnaby Family Institute (Camara, 2005). In Chapter One, Katalin described the anticipated benefits of the AI process:

The research provided an excellent opportunity for staff, volunteers, contractors and board members at BFLI to learn about Appreciative Inquiry, and to put it into practice safely, under clear ethical guidelines while the process was facilitated by a professional trained in Appreciative Inquiry. Specifically, BFLI could discover the benefits of inclusiveness, positive focus and collective knowledge- creation. In addition, AI presented BFLI with the opportunity to “shake up” the institutional energy at a time when organizational renewal was warranted in order to deal with the unintended negative consequences of change the organization experienced during past years. A significant possible benefit of this project could be a positive influence on staff morale…. staff morale appeared to increase while staff turnover decreased (Camara, 2005, pp. 2-3).

Camara’s qualitative Action Research approach employed the “Discovery” stage of the 4-D Appreciative Inquiry cycle “to understand the ‘best of what is and what has been’” (Whitney & Trosten-Bloom, 2010, p. 7). One of the joys of reading this thesis is to “hear” the verbatim voices of the participants in the findings as they gained a growing sense that change was happening while the interviews were taking place. “The Appreciative Inquiry approach changed the language used, the number and the quality of interactions between people and the focus of the conversations” (Camara, 2005, p. 76). This shift in interpersonal relationships is found in both the longer quotations and in the short, direct participant statements about the change they are experiencing in and with one another:

[She] is completely different than the first time I met her. It blows [me away that] she is saying it and that makes me feel like a mirror it’s changing in everybody. So it makes it easier for the group to be able to own the project and be appreciative in any one of these steps.
(Participant ‘B’)

As another participant reflected, “I see a shift in you; it’s the same shift I feel in me” (Participant ‘F’). Another participant summarized, “I understand that someone [who] is really different isn’t necessarily better or worse, and I appreciate that difference in a very different way than I did before” (Participant ‘C’) (Camara, 2005, p. 71).

Camara’s participant quotations in her findings and conclusions convey a strong sense that the AI process has had a genuinely transformational impact in the organization. One section of the study conclusions summarizes a variety of changes in behaviours in the participants that resulted in increased collaboration in the organization and “facilitated the process of releasing leadership skills” (Camara, 2005, p. 78). Indeed, sufficient enthusiasm and support for the Appreciative Inquiry process had built so rapidly that the ED and entire organization agreed to carry on through the other three stages, outside the scope of the project. Camara was able to report on her concluding agency-wide event in her study conclusions:

During the Dream, Design and Delivery phases, people dreamed about the ideal stage of the organization and developed provocative propositions in three themes that came up from the earlier interviews: trust, respect and communication. Based on the collectively formed images and statements, action items were developed in order to move the organization into the direction of the dreamed situation. During this planning phase, many ideas were considered and some of them were chosen by the teams to carry out. People voluntarily assigned themselves to different roles and responsibilities in the delivery process. Those ideas range from newsletters and posters to different social events and relevant training are all under recent implementation.

Beyond the planned implementation process, the philosophy of Appreciative Inquiry has been starting to manifest in many areas of the organizational life. There are ongoing discussions at different levels, within and between different groups of people, about how to embrace Appreciative Inquiry fully. There are regular reflections, within individual and team settings, about experiences in using AI. People aim to discover how to support each other to regularly use appreciative approaches and positive tone of voice and not to fall back to problem-solving methods (Camara, 2005, pp. 83-84).

Additionally, although unreported in the thesis, there were excited phone calls to Katalin internally from the supervisors who were noticing the changes, and emails from Katalin to her RRU academic supervisor about these observed radical shifts in interpersonal relationships, co-operation, energy, and morale. One particular event attests to how remarkable this was for the entire organization. The executive director called Camara’s academic supervisor to invite her to a party the organization was holding in Camara’s honour, to present her with a trophy as a symbol of the contribution the project was making to the organization—again, before the final chapters of Camara’s report were even written.

A recent email from Camara, 10 years after the AI project was completed, described how two of her colleagues took the Justice Institute of BC’s Foundations of Management and Leadership Certificate program and “for their final project they developed an AI introductory course that will be used for all of their new employees. Exciting, yes?” (Camara, K., personal communication, November 28, 2014).

Providence Farm Example

But AI has not been restricted to just the capstone project. In one case, the AI process for students already began in the first year of the MAL program. Each MAL cohort experiences a Leadership ChallengeTM activity, where an invited organization, typically a regional non-profit, presents a current leadership issue or opportunity to a new cohort, gathered at RRU for their first two-week residency. The response to the challenge is taken up by teams of five to seven students. Over the course of a week, the teams interview the organization’s leadership team, investigate their web site, and work out possible approaches for the organization based on the residency leadership literature and the students’ own professional leadership experiences. The organization’s leadership attends a final presentation made by the student teams and gives each team some feedback and appreciation as to the utility or innovativeness of the suggestions. Typically, this hands-on example of authentic, real world, experiential learning in the first residency is highly memorable for students and sets the context for the rest of the program. It is in the context of one of the Leadership Challenges that the elements for an Appreciative Inquiry were established and the Challenge went considerably beyond the usual boundaries of the classroom.

In spring of 2003, a MAL cohort was introduced to Providence Farm (http://providence.bc.ca/), a therapeutic community operating in rural Duncan, BC. The Leadership Challenge focused on Providence Farm’s uncertainty about its future. The organization had been spurred into action to investigate options by an ambitious opportunity presented by the Sisters of St. Ann, who were prepared to donate land if Providence Farm were to build housing. The relationships and insights created from this Leadership Challenge lifted this project into much more than a class activity. The opportunity—and urgency—faced by this non-profit, combined with their strong caring and inclusive values with respect to their clients, created the opportunity for a follow-up Appreciative Inquiry large group event, to help the organization determine its strategic direction and align the stakeholders.

One of us, Ann, working with a learner from the cohort, Lisa Stekelenberg, co-designed and co-facilitated an AI Summit with as much of the Providence Farm community in the room as possible. In this version of the AI Summit, the first two phases of the AI process (“Discovery” and “Dream”) were staged in one meeting and the “Design” phase in a second meeting (Ludema, Whitney, Mohr, & Griffin, 2003). The process began with “Discover”: stakeholder participants at tables, sharing their stories of moments of pride in belonging to the Farm. Participants found recurring themes of memorable stories and what had contributed to previous success: values, types of actions, and support. After conversations about which values and approaches the organization should carry into the future, participants then moved to the “Dream” phase, creating a powerful new organizational vision that incorporated the ideas that emerged from the earlier stage into a new statement that helped push the organizational boundaries further.

One month later, Lisa and Ann co-facilitated a smaller stakeholder group on a “Design” phase AI Summit. Using the approach of Open Space (Owen, 2008), this large group method created a space where planning groups self-organized around participant-generated topics. These topics received broad support from participants working in table groups. These groups reported on their progress periodically to the whole group in plenary sessions. The planning group conversations uncovered previously unnoticed synergies between individual projects that then further expanded their ambitious ideas.

The results of these two meetings exceeded all expectations; the board, clients, funders, therapists, and staff had become aligned on their organization’s revitalized direction. The key stakeholders told Lisa that the new projects the AI process had helped identify were substantial undertakings and significant in the organization’s development (Stekelenburg, L., personal communication, 2003).

AI in Other RRU Programs

AI projects at RRU are not limited to the School of Leadership. The Master’s of Business Administration Executive Management online optional course, Building Sustainable Communities, has students working with Systems Theory, the principles of Community Development, and the theory and practice of Appreciative Inquiry. The same course is offered in the MA-Interdisciplinary Studies program and for students in the Graduate Certificate in Sustainable Community Development. Each student chooses a situation of concern or opportunity, and engages in a current state analysis of their topic. Teams form around a few of these topics and expand the analysis with respect to most appropriate leverage points for change (Meadows, 2008), and then design a plan for an AI initiative involving stakeholders to address the situation. Often the topics arise from actual threats to sustainability in learners’ own home communities or are situations of need about which they are passionate.

As instructors at Royal Roads, we are dedicated to the principle that “progress results because of the powerful connect between knowledge and action” (Coghlin & Brannick, 2008). In the 15 capstone projects that specifically identified as engaging an AI process, Camara’s included, students have consistently found that the stakeholders in their sponsor organizations were enthusiastic and eager to move forward with the changes they have developed during the capstone process. This was the case for the projects from Lisa Stekelenburg, Katalin Camara, and the other MAL, MBA, MAIS, and SCD students who reported that their sponsor organizations had been influenced by an Appreciative Inquiry process, and that there had been evidence of new ideas and changes in the groups’ thinking and communicating in the workplace.

Conclusion

These examples refer to just a few of the ways in which student learning and inquiry using AI are powerful expressions of the RRU Learning and Teaching Model, and in particular, the elements of “create learning conditions that are respectful, welcoming and inclusive,” “facilitates authentic, challenging, collaborative and engaging learning experiences,” and “focusing on applied research-informed learning.” These examples (often) point to transformational learning, deep reflection, and motivation. A wealth of stories of positive change exist within the other capstone projects, assignments, and learner initiatives based on Appreciative Inquiry. For the  authors of this article, the exploration of our shared interest in AI has created a deeper, more nuanced appreciation for the ways in which faculty, students, and all RRU staff engage with one another to promote a positive learning experience and maintain a wonderful orientation to the possibility of innovation that could create systemic positive ripples in our lives and communities.

The opportunity exists for other students, faculty, and staff to try the appreciative approach in their courses and in their organizational lives. Whether there is an opportunity to move forward with a whole AI process, or whether this can only be applied as a guiding metaphor, the opportunity for appreciation for the inspirational and “life-giving” elements of social interaction is a powerful and generative driver of innovation and change, and is a core part of the RRU Learning and Teaching Model.

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