The article focuses on the study that examines AI’s ability to evaluate sentiment and recognize emotions in written statements. The utterances come from interviews with children who were diagnosed with congenital deafness, and who received cochlear implants in the prelingual period. A key criterion for participation was at least five years of implant experience. Children approved to the study were between 7–18 years old. The interviews provided children’s perspectives on daily functioning, helping anticipate emotionally or intellectually challenging situations. This informs descriptions of typical and atypical (emotionally difficult) experiences and strategies for educators, teachers, and psychologists supporting children with cochlear implants. Correctly identifying emotions in children’s narratives is crucial. Projects with extensive interview collections could benefit from AI support if it proves to be a reliable coding engine. A sample of interviews underwent threefold analysis: sentiment evaluation by human coders, multilingual AI (ChatGPT 4.0), and Polish-trained AI (BIELIK-11B-v2). This triangulation aimed to: (a) explore AI-supported vs. human-coded analysis to enhance data-driven decisions, and (b) identify and understand the reliability of different by design AI-engines in analysis made on text specific tasks, (c) present results of the study, and show reflections as well as recommendations.