Understanding one’s own learning (or meta-learning) is a topic of increasing interest in education in the 21st century. Children, who know how they learn, know themselves as learners. This is an important part of becoming a life-long learner in a world where the fast-pace of change creates the illusion that the future is one of greater unknown. Initiatives to help children to ‘learn how they learn’ often teach meta-cognition skills is isolation from the learning process; a strategy for learning that has been found to be ineffective. For children to come to see their role in their own learning, they need to learn about themselves and the unique strengths that they bring to their learning experiences. This is particularly so for children who struggle at school or those who do not believe they have the potential to learn. This qualitative research examines Appreciative Inquiry (AI) as a strengths-based approach to helping children understand their learning. Four 10-year-old children (in year 6) and I engaged together in action research to design and implement an AI intervention, and then evaluate its effectiveness as an approach to learn about their learning. Social constructivist theory is used to understand the children’s shifts in understandings and perceptions, prior to and following the AI intervention. Three key areas provided the conceptual framework for the thesis: social constructivist learning, meta-learning and AI. The research project delivered new findings in several areas. The first main area of findings show that the children experienced significant shifts in their understandings of learning and perceptions of themselves as learners. The formal school context had a powerful influence on their understandings of learning and through the AI they came to view learning as taking place in a much wider context. The formal school context also influenced how they saw themselves as learners and some children believed they that, due to their low grades, they were ‘no good’ at learning. Participation in the AI process helped them to identify their strengths as learners and appreciate how their uniqueness actually benefits their learning. The second main area of findings identified four key factors that are pertinent to the children’s participation in AI: collaborative dialogue, agency in learning, experiential learning and children focus on their strengths as learners. Using AI with children is not as straightforward as using this with adults and, as such, requires that changes be made to the model, the aim being to shift its orientation from being one of a progressive approach to an experiential approach. A key objective of this research was not to impose a learning theory on the children’s experience of learning, instead, through AI theory, the process allowed the children to discover their own learning theories for how they might use their strengths in their future learning. In this way, AI as an approach to meta-learning, was empowering because it worked from the ground up and from the inside out. Additionally, the ‘action’ aspect of the research provided a space for the children to actively make changes in their learning. Children’s perspectives were sought in order that the children themselves could inform the formulation of my research questions. This approach not only respects the children’s capacity to contribute to research, but it provides authentic insights into the experiences of those who are directly influenced by the AI intervention; the children themselves. The children’s perspectives warrant special attention with respect to how they might best condition the design of the methodological approach used in this research to help them come to know their learning and themselves as learners.