Introducing Deeplinq for AML

Solutions / Education

Ground AI in the course and keep educators in charge.

Give learners and staff useful, source-linked assistance while course access, educator judgment, and institutional policy remain visible.

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Reference application / Learner support

Ground assistance in the course and keep educators in charge.

A learning assistant can answer from current course material, explain institutional policy, and prepare routine work. It stays inside the learner’s role and leaves grading, accommodations, and safeguarding decisions with responsible staff.

Illustrative workflow. Deeplinq supplies the governed engine. Your systems and authorized people remain authoritative.

Reference workspaceEDU-COURSE-241
Instructor review

Learning request

Scope locked

  • Learner

    Student 18492

    Enrolled in ECON-241

  • Question

    Extension request

    Assignment 03, due 2 August

  • Course

    Applied economics

    Syllabus and rubric in scope

  • Staff notes

    Not accessible

    No student grant

Course sources

18

current resources

Citations

3

policy and syllabus

Extension

2 days

assistant proposal

Decision

Educator

instructor remains responsible

Authority boundary

The assistant can explain course and institutional guidance. It cannot reveal staff-only material, change a grade, or approve an exception on the educator’s behalf.

Course-grounded support

Use the learner’s real context without widening access.

The workflow retrieves course and policy sources available to the learner, checks the request against the syllabus, and prepares a transparent handoff when staff judgment is needed.

Governed workflowLive policy at every step
  1. 01

    Bind the learner role

    Resolve enrollment and live dataset grants

  2. 02

    Search the course

    Use syllabus, rubric, and published resources

  3. 03

    Check policy

    Cite the institution’s extension guidance

  4. 04

    Prepare an answer

    Explain the rule and available next steps

  5. 05

    Escalate the exception

    Instructor reviews the proposed extension

  6. 06

    Update the record

    The learning system remains authoritative

Learner-visible basis

  • Syllabus

    Late work / paragraph 2

  • Policy

    Short extension guidance / current

  • Private staff data

    Excluded from retrieval

  • Model disclosure

    Model, purpose, and limits visible

Evidence → review → action

Institutional control

Make AI behavior inspectable by students, staff, and operators.

Institutions can publish what the assistant is for, constrain it by role and course, measure model use, and reconstruct the technical control path without logging private conversations as audit evidence.

Operating recordContent-minimized engine audit
  1. 11:08Learner

    Asked about a short assignment extension

    Authenticated course context attached

  2. 11:08Assistant

    Retrieved the syllabus and published policy

    Staff-only notes remained outside scope

  3. 11:09Assistant

    Prepared a two-day extension request

    Evidence and limitations shown to the learner

  4. 11:09Instructor

    Decision is waiting

    No learning record has changed

Control state

  • Role boundary

    Live

    Retrieval follows the authenticated learner

  • Educator authority

    Preserved

    Grades and exceptions stay human-owned

  • AI disclosure

    Visible

    Purpose, model, sources, and limits can be shown

The engine underneath

Support the learner without flattening responsibility.

The reference workflow shows how an institution can embed assistance in real course work while Deeplinq enforces the model, knowledge, role, cost, and audit boundaries.

  1. 01

    Use the real course context

    Retrieve from the learner’s syllabus, rubric, course material, and published policy with bounded citations.

  2. 02

    Respect every role

    Resolve access for the authenticated learner or employee so private staff material never becomes prompt-level guidance.

  3. 03

    Preserve educator authority

    Keep grades, accommodations, exceptions, and safeguarding decisions with responsible staff and authoritative systems.

  4. 04

    Make AI visible

    Expose purpose, model, sources, and limits while measuring actual usage across courses, teams, and models.

Next step

Bring one learner or staff journey.

We will map its course context, institutional sources, role boundaries, educator decisions, system actions, and transparency requirements.

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