Artificial intelligence (AI) has reshaped contemporary approaches to teaching, assessment, and feedback. Most AI systems provide reactive feedback, offering instant answers that reduce learners’ cognitive engagement and sense of agency. In contrast, Mili was developed as a proactive pedagogical intelligence that asks guiding questions and encourages learners to construct their own responses. Through this design, feedback becomes a process of learning rather than an evaluative mechanism. Mili is a Hebrew-language educational chatbot grounded in principles of dialogic feedback, pedagogical mediation, and literacy resilience. Its goal is to create a metacognitive literacy dialogue in which questions replace answers and learning becomes an act of reflection and self-inquiry. The development followed a Design-Based Research approach involving iterative cycles of design, training, and testing. At each stage, pedagogical prompts were crafted to simulate authentic teacher–learner dialogue, including clarifying questions, pedagogical delay, and emotional reinforcement. This process enabled an exploration of how AI can mediate feedback that stimulates deeper cognitive engagement. The resulting model demonstrates proactive dialogic feedback in which AI does not simply respond but initiates reflective dialogue. Simulated interactions with Mili reveal how such feedback supports the three dimensions of literacy resilience: linguistic-cognitive, metacognitive, and emotional. Mili represents a conceptual shift in AI-based feedback, moving from response to process, from outcome to mediation, and from reactive AI to learning-generative AI. The study makes a theoretical contribution by articulating a model of pedagogically mediated AI and a practical contribution by developing a feedback tool that fosters inquiry, reflection, and literacy resilience in learners and teachers.
Alisa Amir (2026) studied this question.