Key Points

  1. A guest commentary by Tutorela co-founder Roee Naim argues that the gap between tutoring sessions — not lesson quality — is where learning most often breaks down.
  2. The piece contends that practice without timely feedback can entrench misconceptions, so volume of homework matters less than rapid correction and adaptive follow-up.
  3. It positions AI as between-session support that also feeds diagnostic data back to human tutors, a model the author says makes paid teaching time more valuable rather than redundant.

The Case for the Six Days Between Lessons

The scenario is familiar to almost any parent who has paid for tutoring: a lesson goes well, the student follows the explanation, works through problems with the teacher and leaves confident. Three days later, facing a similar problem alone, they are stuck again. The instinct is to blame the lesson — too fast, wrong teacher. According to a commentary published by eSchool News, neither is usually the problem.

The author, Roee Naim, co-founder of the U.S. tutoring marketplace Tutorela, draws a distinction between understanding something with a teacher's guidance and being able to retrieve and apply it independently. During a lesson, a teacher supplies hints at the right moment, catches errors before they compound and adjusts pace. That scaffolding makes material feel clear, but it disappears the moment the session ends. What remains is whatever the student can recall unaided — and research on the forgetting curve, the article notes, shows how quickly unreinforced learning fades.

Naim calls this stretch the "between-lessons gap." A student might spend one or two hours a week with a teacher; the other six days, he argues, may matter just as much. Yet education systems and learning technologies have invested heavily in improving the lesson itself — pedagogy, platforms, teacher training — with far less attention to what happens in between. Students forget, get stuck, postpone practice or skip it, and arrive at the next session with the teacher having no idea what occurred in the interim.

The proposed fix is not simply more homework. Practice without feedback, the piece warns, can reinforce the wrong things: a student can rehearse the same misconception repeatedly, hit the same wall and conclude they are bad at the subject, or gravitate toward problems they already know how to solve because it feels productive. The variable that determines whether practice works, Naim writes, is not volume but whether the student learns quickly when they are wrong and whether what follows adapts to that.

What Tutorela's Argument Reveals About the Tutoring Market

An Argument Shaped by Tutorela's Business Model

The commentary is not neutral analysis; it is a positioning piece from an executive whose company sells exactly the model he describes — human tutors combined with AI-powered support between sessions. That does not make the underlying learning-science claims wrong, but readers should weigh the source. Naim's previous venture, Limud Naim, says it has facilitated more than 500,000 private lessons, so the argument reflects operational experience in tutoring marketplaces rather than independent academic research. No external studies, named researchers or data sets are cited in the article; the forgetting-curve reference is made generically.

Where the Tutoring Market Is Actually Moving

The commercial logic behind the piece is straightforward. Tutoring is a time-constrained business: revenue scales with tutor hours, and tutor hours are expensive and finite. If AI can handle practice generation, answer checking and error tracking between sessions, a marketplace can serve more students per tutor without lowering the human touch that parents pay for. The article's claim that diagnosis takes less of each paid hour and teaching takes more is effectively a pitch for higher tutor utilization — a margin argument dressed as pedagogy.

Why the Feedback Loop Is the Hard Part

The genuinely difficult element is the data flow back to the teacher. Generating practice questions with AI is now commoditized; reliably detecting recurring errors, distinguishing a careless slip from a conceptual gap, and routing that insight into a usable pre-lesson briefing is not. The article asserts this happens but offers no detail on accuracy, cost or how tutors actually consume the output. For context, this is the same reliability problem that has slowed adoption of AI tutoring tools more broadly: the technology is most useful precisely where errors are most consequential.

What the Piece Leaves Unaddressed

Naim concedes that motivation, encouragement and accountability remain human tasks, and that technology works best supporting teachers rather than replacing them. That concession is sensible but also sidesteps the harder questions for the sector: whether families will pay more for between-session support, whether schools and districts will procure it, and whether the claimed learning gains can be demonstrated rather than asserted. Until those are answered with evidence, the between-lessons gap is a compelling framing more than a proven market.

What This Means for Parents, Tutors and Edtech Builders

The most useful idea here is a diagnostic question, not a product recommendation: if your child can solve problems with a tutor but not alone three days later, the problem is likely retrieval and feedback, not teaching quality.

  • For parents paying for tutoring: ask the tutor what happens between sessions. If the answer is "homework" with no mechanism for checking it before the next lesson, the six-day gap is unmanaged. Request that practice be reviewed — even briefly — before the next paid hour, so the session starts with diagnosis rather than reconstruction.
  • For tutors: the article's practical claim is that arriving already knowing what the student attempted, got wrong and abandoned changes how the hour is spent. Even a simple shared log of errors between sessions captures part of that value without new software.
  • For edtech builders and investors: the differentiated capability is not practice generation, which is commoditized, but reliable error-pattern detection and a teacher-facing summary that is accurate enough to act on. That is where the article's argument is strongest and its evidence thinnest.
  • What to watch: whether Tutorela or comparable marketplaces publish measurable outcomes — retention, score improvement, tutor hours saved per student — rather than conceptual arguments. That evidence, not the framing, will determine whether between-session AI becomes a standard feature or a marketing line.