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Everything you need for self-directed learning — all powered by AI, all built for your goals.
Tells us what you already know. The AI builds a course that skips familiar material and focuses on gaps — no wasted time.
Prior knowledge assessment is the strongest predictor of learning outcomes (Dochy et al., 1999). Adaptive pretesting reduces time-to-competency by up to 40%.
A 5–15 milestone path sequenced to your background and goal. Generated fresh for every topic, every learner.
Personalised sequencing improves transfer test performance by g = 0.35 vs. fixed curricula (VanLehn, 2011). Mastery learning — progress at your pace — has an effect size of d = 0.58 (Kulik & Kulik, 1990).
Each milestone combines text explanations, interactive diagrams, and knowledge checks — generated for your specific goal as you progress.
Practice testing (active recall) has a high-utility rating of d = 0.74 (Dunlosky et al., 2013). Combining text with interactive visuals reduces cognitive load and improves retention (Mayer, 2021).
Struggling with a concept? The roadmap adjusts. Flying through? It accelerates. Built-in checks validate understanding before moving on.
Mastery learning — advancing only when a concept is understood — produces effect sizes of d = 0.65–1.18 across domains (Bloom, 1984; Guskey, 2007).
Ask questions, dig deeper, get clarifications — the AI tutor is available at every step to help you understand.
Intelligent tutoring systems improve learning outcomes by g = 0.41 (Steenbergen-Hu & Cooper, 2013). Process-focused feedback — explaining why — is significantly more effective than correct/incorrect feedback (Brummer et al., 2024).
See completed milestones, track your pace, and pick up where you left off. Visual progress map of your learning journey.
Self-monitoring and visible progress feedback improve self-regulated learning outcomes at d = 0.50–0.69 (Hattie, 2009; Schunk & Zimmerman, 2008).
Reinforce what you learn with periodic review sessions at optimal intervals. The SM-2 algorithm schedules each review just before you'd forget.
Distributed practice — spacing reviews over time — has the highest utility rating of any learning technique at d = 0.85 (Dunlosky et al., 2013). The SM-2 algorithm powers this with scientifically calibrated intervals.
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