Data as of Oct 12, 2026A topic in AI Socratic Tutoring Platforms.
Reviewed by Dimitry Apollonsky
AI summary
Khanmigo holds a clear lead for supervised AI tutoring, frequently chosen for guiding students toward answers using Socratic questioning under educator oversight. SchoolAI serves as an alternative for educators seeking real-time visibility into student interactions.
asking probing questions to support mastery learning alongside teacher controls
real-time monitoring of student interactions to protect academic integrity
supporting guided mastery learning within supervised educational environments
helping educators manage and guide student artificial intelligence sessions
custom helper tools that keep AI focused on specific objectives
Mentioned in · Jul 7 – Oct 12, 2026
ChatGPT SearchOct 4, 2026
I looked for AI tutoring platforms with instructor controls over (1) allowed sources/knowledge, (2) visibility into student conversations, and (3) when/how hints appear. The strongest matches I found are below.
| Platform | Constrain sources / grounding | Review student conversations | Control hint behavior |
|---|---|---|---|
| Polya | Yes — instructors can ground the tutor in assigned course materials (readings, slides, lecture recordings); answers include source citations. |
Google AI ModeOct 4, 2026
Google AI ModeOct 12, 2026
| Yes — instructor-facing transparency is built in; students see the active policy and instructors can govern course assistance behavior. |
| Yes — course policies include Open, Guided, Practice, and Review modes. Guided provides hints/steps first; Practice requires student commitment before answers; Review allows fuller solutions after deadlines. |
| HiTA.ai | Yes — uses instructor-provided course readings, lectures, and assignments. | Yes — instructors can review student–AI interactions and analyze misconceptions. | Yes — instructors configure scaffolding levels, such as direct answers, hints, or guiding questions. |
| Ofnil Edu | Yes — teachers define solution paths and task guidance. | Yes — teachers can see student progress and chat history through dashboards. | Yes — teachers can set AI level: disabled, limited hints, guided learning, or open exploration. |
| Bloom AI | Yes — educators control knowledge-base documents, instructions, blocked content, and topic boundaries. | Public documentation emphasizes educator controls, though conversation-review details appear less central than in some other tools. | Partly — can guide behavior through instructions and blocked-content rules, including avoiding direct answers for protected assessments. |
| CodeHS | Limited to its coding/course environment rather than general course-source grounding. | Yes — teachers can view AI hint usage, conversation threads, and hint logs. | Yes — teachers can enable/disable AI hints and monitor hint activity. |
| Tutory | Yes — uses teacher materials and teaching sequence rather than a generic chatbot approach. | Yes — conversations are available to teachers; students are informed transcripts can be read. | Yes — designed around questioning and guided progress rather than giving answers directly. |
If you are evaluating these for a school or university procurement, the key questions to ask vendors are:
Several prominent AI tutoring and classroom-management platforms give instructors granular control over source material constraints, student conversation auditing , and pedagogical settings (such as hint structures and scaffolding).[](https://thelearningstandard.org/apps/schoolai)
The leading platforms with these comprehensive instructor controls include:
If you are looking for a platform tailored to a specific grade level (K-12 vs. Higher Ed) or a particular subject area (like coding vs. humanities), let me know and I can narrow down which option fits your workflow best.
Several K-12 and higher-ed AI platforms stand out for giving educators robust controls across source uploading, tracking, and Socratic/hint behavior. While few platforms expose a literal slider labeled "hint frequency" (opting instead for behavioral prompts and system instructions), the following tools meet your criteria:
Would you like to explore pricing/school-wide licensing details for these platforms, or do you need help drafting a custom prompt/hint structure for one of them?
ChatGPT SearchOct 5, 2026
Yes. Based on current product documentation, SchoolAI, CodeHS Smart Hints, and Goodnotes Education are the strongest matches—but they differ substantially in how much control they give educators.
| Tool | Control source materials | See student interactions | Configure hint behavior/frequency | Overall fit |
|---|---|---|---|---|
| SchoolAI | — teachers can upload lesson materials, rubrics, handbooks, etc., or use web sources as context. |
Yes, purpose-built educational platforms have evolved to give educators this exact tier of granular control. Rather than acting as open-ended chatbots, modern classroom-focused AI systems use constrained architectures (often leveraging Retrieval-Augmented Generation or strict system prompts) to ensure teachers remain in the driver's seat.[](https://www.apporto.com/how-to-create-an-ai-tutor)
Platform capabilities map directly to your requirements:
Restricting Knowledge Bases: Platforms allow teachers to upload specific PDFs, curriculum docs, textbook chapters, or anchor texts. The AI is hard-bound to only source answers and generate contexts from those files, actively refusing out-of-bounds queries or external hallucinations.[](https://www.apporto.com/how-to-create-an-ai-tutor)
Auditing Student Interactions: Real-time and retrospective teacher dashboards log every single prompt, response, and misstep. Educators can view active chat streams as they happen, flag concerning or off-topic dialogue, and inspect decision logs.[](https://schoolai.com/blog/school-leaders-audit-edtech-algorithmic-bias)
Configuring Hint Timing & Pedagogical Guardrails: Teachers can toggle constraints that stop the AI from giving away direct answers. Instead, they configure parameters to enforce Socratic questioning, time-delay hints until a student has made a genuine attempt, or scale the depth of knowledge required before a clue drops.[](https://arxiv.org/html/2604.16738v1)[[2](https://schoolai.com/blog/school-leaders-audit-edtech-algorithmic-bias)](https://www.apporto.com/how-to-create-an-ai-tutor)
SchoolAI (Spaces): Highly popular in K-12 and institutional settings. It lets teachers build custom "Spaces" where they define the AI persona, restrict the scope to specific learning objectives or uploaded materials, monitor every student chat in real-time, and implement "no direct answer" rules.[](https://help.schoolai.com/en/articles/15538519-getting-started-with-spaces)[[2](https://schoolai.com/blog/school-leaders-audit-edtech-algorithmic-bias)](https://schoolai.com/blog/school-leaders-audit-edtech-algorithmic-bias)
Apporto (CoTutor): Focuses heavily on grounding large language models via RAG with curated, instructor-approved datasets to prevent hallucinations and narrow knowledge boundaries while maintaining full visibility dashboards for instructors.[](https://www.apporto.com/how-to-create-an-ai-tutor)
Khanmigo by Khan Academy: Designed specifically to act as a guide rather than an answer key, heavily restricting outputs to Socratic hints, though it operates primarily within Khan's pre-mapped curriculum framework rather than an open teacher-uploaded knowledge base.
If you'd like to narrow this down, let me know:
ChatGPT SearchOct 11, 2026
Yes. Several AI-driven tutoring systems now offer meaningful teacher controls over what the AI can teach, visibility into student interactions, and the level of assistance students receive. The important distinction is that these capabilities are not always available together: some platforms let teachers restrict content and review transcripts, while others offer configurable hint behavior but do not document precise, time-based hint triggers.
Based on the product documentation I found, these are the strongest candidates to evaluate.
(Strong content controls)
Teachers can build a tutor from selected documents, set topic restrictions, block specific assessment content, and configure pedagogical behavior, including how much help the tutor gives and whether it reveals answers.
The leading AI-supported ecosystem explicitly engineered for mastery learning paired with robust teacher controls and dashboards is Khanmigo for Teachers (by Khan Academy).[](https://www.khanacademy.org/khan-for-educators/k4e-us-demo/xb78db74671c953a7:get-to-know-khan-academy/xb78db74671c953a7:welcome-to-khan-academy-1/v/the-ai-enhanced-mastery-learning-approach)[[2](https://www.khanacademy.org/khan-for-educators/k4e-us-demo/xb78db74671c953a7:get-to-know-khan-academy/xb78db74671c953a7:welcome-to-khan-academy-1/v/the-mastery-learning-journey)](https://www.khanacademy.org/khan-for-educators/k4e-us-demo/xb78db74671c953a7:get-to-know-khan-academy/xb78db74671c953a7:welcome-to-khan-academy-1/v/the-mastery-learning-journey)
Mastery-Based Progression: Rather than moving on a fixed calendar schedule, students progress through skills and unit missions by demonstrating true comprehension via iterative practice, quizzes, and challenges.[](https://www.khanacademy.org/khan-for-educators/k4e-us-demo/xb78db74671c953a7:get-to-know-khan-academy/xb78db74671c953a7:welcome-to-khan-academy-1/v/the-ai-enhanced-mastery-learning-approach)
Socratic AI Guardrails: The AI assistant is explicitly prompted not to give students direct answers. Instead, it uses guided scaffolding to foster productive struggle and conceptual clarity.[](https://www.forasoft.com/blog/article/ai-tutors-adaptive-learning-2026)[[2](https://www.khanacademy.org/khan-for-educators/k4e-us-demo/xb78db74671c953a7:get-to-know-khan-academy/xb78db74671c953a7:welcome-to-khan-academy-1/v/the-mastery-learning-journey)](https://www.youtube.com/watch?v=BhqIZ6o30Jk)
Comprehensive Teacher Dashboards: Teachers retain oversight via real-time data tracking. The dashboard highlights specific student struggles, skill gaps, and milestones achieved, allowing educators to easily group students for targeted intervention or assign specific content paths.[](https://www.khanacademy.org/khan-for-educators/k4e-us-demo/xb78db74671c953a7:get-to-know-khan-academy/xb78db74671c953a7:welcome-to-khan-academy-1/v/the-ai-enhanced-mastery-learning-approach)
Custom Activity Generation: Educators can use Khanmigo's teacher tools to generate customized AI activities, discussion prompts, and exit tickets tightly aligned to their specific instructional sequence.[](https://www.khanacademy.org/khan-for-educators/k4e-us-demo/xb78db74671c953a7:get-to-know-khan-academy/xb78db74671c953a7:welcome-to-khan-academy-1/v/the-mastery-learning-journey)
ASSISTments : A research-backed math practice platform that uses AI strictly to assist teachers in interpreting class-wide misconceptions and scaffolding student persistence without handing out direct answers. The teacher remains fully in control of the assignment flow and interventions.[](https://www.assistments.org/ai-methods-and-approaches)
Flint K12 : Built explicitly for schools, allowing teachers to spin up custom AI tutors tied to precise curricula, view student chat logs, and strictly govern what topics or files the AI is allowed to reference.[](https://medium.com/inspired-ideas-prek-12/how-ai-is-changing-the-script-in-education-ddc21e38a65f)
If you want to narrow this down, let me know:
I can give you a more tailored breakdown of setup and pricing for your classroom.
ChatGPT SearchOct 12, 2026
The best choice depends on whether you prioritize mastery-based learning, detailed teacher oversight, or direct control over the AI tutor. My top recommendation for a general K–12 learning platform is Khanmigo; for math-specific mastery, Carnegie Learning MATHia is also worth considering.
1. Khanmigo — best all-around option
Combines guided, Socratic tutoring with Khan Academy's skill-mastery curriculum. Teachers can review mastery progress, assignment completion, and student activity.
| Strong — Mission Control/Student Insights can expose full chat transcripts and activity. |
| Partial — teachers can specify how the AI should provide hints/feedback in its instructions, but I did not find a documented numeric “hints per day” or explicit hint-frequency setting. |
| Best for broad curriculum/source control + oversight |
| CodeHS Smart Hints | Moderate — hints are grounded in the student's assignment/code and autograder context rather than a general teacher-curated source library. | Excellent — teachers can review every hint conversation and usage analytics. | Excellent — teachers control access, while Smart Hints has a defined 5-hints-per-day student limit. | Best match if hint-frequency control is essential, especially for coding |
| Goodnotes Education + AI Hints | Strong — teachers can build lessons from PDFs, Google Drive, Word/PowerPoint, images, etc., and AI uses the student's work plus teacher-set rubrics. | Strong — teachers can view individual students' interactions with hints, including how many hints were requested. | Moderate — teachers approve multiple hints and students reveal them one at a time; I found no documented configurable numeric frequency/limit. | Good for worksheet/document-centered tutoring |
| Khanmigo | Moderate — deeply tied to Khan Academy's content library rather than arbitrary teacher-controlled source sets. | Excellent — teachers can inspect full student chat histories and receive moderation alerts. | Weak for this criterion — its Socratic “don't just give the answer” behavior is built in, but I couldn't find a teacher control for hint frequency. | Excellent tutor/visibility, less configurable |
If your three requirements are strict—teacher-selected sources + transcript-level visibility + an explicit way to control how often students can get hints—CodeHS is the clearest documented match, although its source control is assignment/code-centric rather than a general “upload any curriculum and ground the tutor in it” model.
If source-material control is the priority, I'd put SchoolAI first. Teachers can upload their own instructional files and configure the AI's behavior, while Mission Control provides unusually strong visibility into student conversations.
If you're looking for a tutor that can be tightly constrained to teacher-created course content, there's also a newer category worth investigating: AIshie says its course agents search only published course materials and identify the materials used in their answers. However, I haven't found enough documentation yet to establish an educator-configurable hint frequency, so I wouldn't count it as a confirmed three-for-three match.
Bottom line:
Best for: teachers who want to design and tune a subject-specific tutor.
(Strong auditability)
Honen's K–12 offering documents approved-material-only responses, configurable district and course guardrails, and teacher/admin access to student conversation transcripts.
Best for: schools and districts prioritizing governance, oversight, and consistent rules.
(Strong course configuration)
Instructors can select which Canvas materials, Google Drive folders, and websites the tutor uses, then customize its system prompt. Teaching styles range from Socratic guidance to direct answers, with instructions for hints, scaffolding, and assessment boundaries.
Best for: higher education courses already using Canvas.
ed360 describes curriculum-grounded student tutors, teacher-defined operating policies, live session monitoring, alerts when students stall, and the ability to pause sessions, inject prompts, or adjust scope.
Best for: K–12 settings where teachers want real-time intervention.
This is a more constrained tutoring approach: students select from four preset prompts, hints point toward the next step rather than giving the answer, and teachers can review the prompt selected and the hint received. Teachers control whether the feature is enabled.
Best for: computer science assignments where bounded help is preferable to unrestricted chat.
| Platform | Restrict knowledge base | Audit student interactions | Configure hint timing |
|---|---|---|---|
| Bloom AI | Yes | Analytics confirmed; full transcripts unclear | Hint behavior, not timing |
| Honen | Yes | Transcripts available | Behavioral rules, not timing |
| TritonAI Tutor | Yes | Verify | Prompt-based |
| ed360 | Yes | Live monitoring | Verify |
| CodeHS Smart Hints | Limited/verify | Hint activity | Preset workflow |
Assessment based on publicly available documentation reviewed; a minus indicates the specific capability is not confirmed, not necessarily absent.
One final distinction matters: if by audit you mean reviewing every student message, the AI's response, timestamps, and the knowledge sources used for each answer, confirm all four separately. Analytics dashboards alone are not a complete interaction audit.
When you say “configure hint timing,” do you mean hints appear after a set amount of time, after a certain number of incorrect attempts, or at a teacher-defined point in a lesson? That distinction would narrow the shortlist considerably.
Best for: K–12 teachers using Khan Academy.
2. Carnegie Learning MATHia — best for rigorous math mastery
Tracks proficiency skill by skill, adapts practice to students' needs, and provides teacher reports and LiveLab monitoring with alerts for students who need help.
Best for: Middle and high school mathematics.
3. SchoolAI — best for direct teacher control
Teachers design custom AI learning environments, define learning objectives and response boundaries, monitor student conversations in real time, and track learning outcomes. Its Spaces can adapt difficulty as students demonstrate mastery.
Best for: Teachers who want to design and supervise AI-led lessons.
If you tell me whether you're choosing for an individual student, a classroom teacher, or a school/district, I can narrow this down to the strongest fit.
