William Dekker // VP of Product Innovation // Efekta Education
Efekta R&D Blog // 16th July 2026
Scaling human-quality feedback with agentic AI
By combining LLM-powered feedback generation with agentic capabilities, it becomes possible to create self-access learning experiences in which feedback is not a one-off message but an ongoing, personalised conversation that guides learners from information to understanding and action. The goal is not simply to automate comments, but to model the pedagogical strengths of effective teachers in digital environments at far greater scale.
Feedback as a driver of learning
Feedback is one of the strongest influences on student achievement when it is designed and used well. Contemporary work in education increasingly frames effective feedback as an interactive dialogue rather than static delivery. Instead of a one-way stream of comments, it becomes a process in which learners interpret, question, and respond to information about their work. This creates opportunities for reflection, clarification, and re-engagement, helping learners build the agency to use feedback independently over time.
Providing quality feedback in the English language classroom
Providing high-quality, dialogic feedback consistently is difficult, especially at scale. One effective response is private tutoring, where the teacher can focus closely on a single learner’s needs through a task–feedback–re-task sequence. In Hyperclass virtual tutoring sessions, learners complete an authentic communicative task, such as drafting an email or taking part in a role-play. This is followed by a short reflective discussion to dicuss strengths and areas for improvement. The sequence concludes with an opportunity to revise or repeat the task and apply the feedback, followed by a final reflective discussion.
From effective teaching to scalable feedback
While effective, this teaching model is hard to scale because it depends on sustained access to expert teacher time. Large language models create a practical opportunity to extend this approach by generating timely, personalised feedback on open-ended learner work. The opportunity, however, is not simply to automate feedback; it is to design AI-led experiences that preserve the pedagogical strengths of effective teachers, especially their ability to explain, clarify and expand on feedback to support learning.
This is the thinking behind our agentic AI for self-access language practice. The aim is to design AI-led task–feedback–re-task sequences that reflect effective teaching. The AI instructor facilitates completion of task, then engages the learner in a dialogic feedback process, prompting the learner to reflect, interpret, decide what to revise first, and then apply that insight in a structured re-task. In this way, the agent models effective teaching practice not only by identifying what matters, but by turning feedback into a guided conversation that supports understanding, choice, and action.
