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A college course inside a K-12 environment
A dual-enrollment course is not simply a high-school class with college credit attached. The student is participating in postsecondary education while remaining in a K-12 setting. AI expectations can therefore depend on the college, department, course, instructor, assignment, tool, account, network, and information involved.
The practical solution is clear alignment + AI literacy. Preserve college-level expectations, give high-school students unusually clear directions, and teach them how to make sound AI decisions when no one is watching.
College-level rigor remains
Mississippi’s current manual states that postsecondary academic standards apply whether the course is on campus, online, or at a secondary school.
K-12 controls remain real
A district may control its devices, network, approved applications, accounts, filtering, privacy process, and local conduct expectations.
The student should not arbitrate
Partners should identify and resolve conflicting requirements, document the answer, and give the student one clear instruction.
Coordinate before the course begins
Download the AI + Dual Enrollment Coordination Toolkit
Use the partnership snapshot, responsibility matrix, readiness checks, copy-ready communications, conflict-resolution record, and annual review worksheet to give students one clear set of AI expectations.
Permission must be visible
Use a clear AI permission model
MAIN recommends this adaptable four-status vocabulary. It is not a statewide mandate, and a course or institution may use other terms.
AI required
Students must use the named approved tool or an approved alternative for a defined learning purpose. Teach the skill and limits before assessing it.
AI permitted
Students may use AI within the stated boundaries and remain responsible for verification, disclosure, citation, and their own reasoning.
AI limited
AI is allowed only for specified steps, such as brainstorming, tutoring, feedback, debugging, or revision. Name what the student must do personally.
AI not permitted
Students complete the activity without generative AI because independent performance is the learning objective. Define the boundary and reason.
The syllabus is not enough
Communicate at four layers: institutional policy, course/syllabus, LMS or course space, and assignment instructions. A general statement cannot answer every assignment-level question.
Every assignment should answer
- May AI be used, and which tools?
- For what steps, and what may not be delegated?
- What disclosure, citation, or process evidence is required?
- What must the student verify and be able to explain?
What am I responsible for?
Responsibility matrix
“Leads” describes a common operating role, not a universal legal conclusion. Apply the current partnership agreement and institutional policies.
On a phone or with keyboard focus: swipe or scroll horizontally to view every role.
| Issue | College / university | Instructor of Record | K-12 partner | Facilitator / coordinator | Student |
|---|---|---|---|---|---|
| Institutional AI policy | Defines institution-wide requirements. | Applies policy within the course. | Reviews fit with district requirements. | Understands both systems. | Follows applicable directions. |
| Course and assignment permissions | Sets academic and department boundaries. | Typically leads: states clear, timely rules. | Flags access, privacy, or technology conflicts. | Reinforces; does not independently rewrite rules. | Checks before use; asks when unclear. |
| Academic standards and integrity | Typically leads: defines course standards and process. | Uses established procedures and evidence. | Coordinates where local conduct also applies. | Reports concerns through the defined path. | Submits work they can explain; participates fairly. |
| Approved AI tools | Approves tools for institutional academic use. | Requires only approved, appropriate tools. | Leads local access: devices, network, accounts, filtering. | Confirms classroom availability. | Uses authorized access; does not bypass controls. |
| Privacy and student data | Applies postsecondary privacy, security, and vendor review. | Uses minimum necessary data and approved environments. | Applies K-12 privacy, consent, security, and vendor processes. | Prevents unnecessary uploads and escalates questions. | Does not enter sensitive or restricted information. |
| Accessibility and equitable access | Provides institutional accessibility process and alternatives. | Designs an accessible, no-surprise activity. | Confirms device, network, and local support. | Identifies barriers early. | Reports barriers promptly through the right channel. |
| AI literacy | Provides college-ready expectations and resources. | Teaches discipline- and assignment-relevant judgment. | Builds age-appropriate digital and AI literacy. | Connects the two learning environments. | Practices, verifies, reflects, and improves. |
| Escalation and communication | Names a dual-enrollment and academic contact. | Clarifies course intent and academic requirements. | Names district/school contact and family process. | Coordinates: documents and routes the issue. | Receives one clear answer; is not the arbitrator. |
Before the course starts
Four readiness checklists
Use these during onboarding, annual partnership review, and course setup.
Postsecondary partner
- Identify AI, integrity, privacy, security, accessibility, and approved-tool requirements.
- Define Instructor of Record and facilitator responsibilities.
- Provide course and assignment communication guidance.
- Establish misuse, escalation, and contact processes.
- Share student-facing AI resources and literacy opportunities.
K-12 partner
- Review college requirements for conflict with district technology and conduct rules.
- Confirm approved access, age/account terms, privacy, cost, and accessibility.
- Train facilitators and name a coordinator.
- Communicate expectations to students and families.
- Resolve conflicting academic instructions with the college rather than improvising.
Instructor of Record
- Define course- and assignment-level AI status.
- Name permitted and restricted uses, disclosure, citation, and verification.
- Coordinate with the facilitator before launch.
- Model appropriate AI use where relevant.
- Use established academic-integrity procedures, not ad hoc AI penalties.
High-school facilitator / instructor
- Understand the postsecondary course requirements.
- Reinforce rather than independently change the college course rule.
- Help students see differences between high-school and college expectations.
- Escalate ambiguity; do not improvise policy.
- Model responsible use and build AI literacy, not only compliance.
A reusable student decision check
Before you use AI
AI can assist your work. You remain responsible for your work.
Check
Does my instructor allow AI for this assignment and this step?
Know
Which tools and kinds of help are permitted?
Protect
Am I uploading anything private, restricted, confidential, or someone else’s?
Verify
Can I independently confirm the claims, sources, calculations, and code?
Disclose
Must I explain, attribute, or cite how I used AI?
Own
Can I explain and defend everything I submit?
College + workforce readiness
Teach AI literacy, not just AI compliance
The objective is judgment, not proficiency with one chatbot. Dual enrollment is a transition point from K-12 to college to work, making it an unusually valuable place to teach responsible habits.
Understand
How generative systems produce outputs; why confidence is not accuracy; and how hallucinations, false citations, bias, and missing context appear.
Practice
Prompting, AI-assisted studying, research-keyword development, comparison, revision feedback, verification, disclosure, and discipline-relevant use.
Judge
When AI helps, when it weakens learning, what must remain human, what information is unsafe to share, and when to ask a qualified person.
Examples that may support learning when authorized
- Ask for another explanation or practice questions.
- Build a study guide from the student’s own notes.
- Brainstorm topics, research keywords, or questions for an instructor.
- Compare problem-solving approaches or debug code.
- Practice language, interviews, or presentations.
- Request feedback on clarity before the student revises.
These examples never override course or assignment instructions.
Higher-risk or often restricted uses
- Generate an entire assignment or answer a graded assessment.
- Present generated discussion content as personal thought.
- Fabricate citations or paraphrase to conceal copying.
- Upload student records, classmates’ work, or unpublished assessments.
- Allow an agent to submit work or act in an academic system.
- Use AI where the instructions prohibit it.
Some uses may be legitimate in a carefully designed activity; require explicit authorization and controls.
Disclosure is not always citation. Disclosure explains whether and how AI contributed. Citation or attribution credits a source or tool under the instructor, discipline, institution, or required style. Students should follow the stated requirements; this guide creates no universal AI citation rule.
Integrity with due process
AI use, AI detection, and misconduct are different
Authorized AI-assisted learning, unauthorized use, and an institutional finding of misconduct are not the same category.
AI use
May be required, permitted, limited, or prohibited depending on the learning objective and governing instructions. Availability does not equal permission.
AI detection
A probabilistic estimate that text has characteristics associated with generated or altered content. It does not determine authorization, intent, or misconduct.
Academic misconduct
A determination under applicable policy and procedure based on the actual expectation, conduct, evidence, context, and fair review.
What current evidence supports
Peer-reviewed 2026 studies reach different performance results across tools, datasets, genres, languages, model generations, and mixed human/AI writing. One June study found strong performance from one tool on its controlled dataset while other tools underdetected; another found two commercial detectors insufficient for high-stakes judgments on authentic EFL writing.
The careful conclusion is not “detectors never work.” It is that performance is uneven and changes, and a score cannot establish whether use was authorized or whether misconduct occurred.
A fair operating rule
Do not use a detector score as standalone proof or the sole basis for a grade, accusation, or penalty. Even Turnitin’s current guidance says its model may misidentify text and requires further scrutiny, human judgment, and institutional policy.
Validate any product in the institution’s own context, document limits, monitor differential impacts, and preserve the established academic-integrity process.
Rethink the investment: institutions should ask whether additional time and money spent on detection would create more educational value if invested in AI literacy, faculty development, student orientation, equitable approved access, assessment design, mentoring, verification, and fair integrity processes. Current evidence does not support a universal cost-effectiveness figure, so this is a governance question—not a quantified claim.
When misuse is suspected
Review the learning and the evidence
Set clear boundaries. Teach responsible use. Design for learning. Address actual misconduct through the established process.
Review the expectation
Was the rule clear about permission, boundaries, disclosure, and process evidence? Address material ambiguity before assuming misconduct.
Review the work
Look for substantive issues: fabricated sources, unsupported claims, factual inconsistency, missing required steps, or unexplained reasoning—not writing style alone.
Review the process
When appropriate, inspect naturally produced outlines, notes, drafts, sources, calculations, research logs, or code/version history. Do not make surveillance the assignment.
Talk with the student
Ask the student to explain the argument, sources, calculations, code, revisions, and any AI use. Use conversation to understand learning, not as an ambush.
Teach when appropriate
If policy allows, ambiguity or a developing skill may call for clarification, verification practice, reflection, explanation, revision, or resubmission.
Use established procedure
When evidence supports a potential violation, follow the college’s applicable integrity process and any required coordination. Do not invent an AI-only punishment system.
From detection to education
Design for visible learning, not continuous surveillance
A useful progression is: Clarify → Teach → Model → Practice → Reflect → Verify → Improve.
Make thinking visible
Use selected proposals, annotated sources, checkpoints, drafts, decision notes, oral explanations, demonstrations, portfolios, presentations, or version history when they serve the learning objective.
Use authentic context
Connect work to local issues, course discussions, labs, field observations, unique data, real constraints, or personalized application.
Assess judgment
Ask students to verify claims, critique an AI response, compare approaches, identify bias, correct errors, and explain what they accepted, rejected, or changed.
AI-integrated assignment ideas, when pedagogically appropriate
- Generate a response, locate its errors, and repair it with verified sources.
- Compare AI-suggested sources with scholarly sources the student actually opens and evaluates.
- Improve a weak generated argument and explain every meaningful revision.
- Ask AI to explain a concept, then independently verify and correct the explanation.
- Compare the student’s solution with an AI-generated solution.
- Document prompts, outputs, verification, revisions, and final judgment.
- Compare outputs from two systems and explain meaningful differences.
- Use AI to generate questions, then decide which actually demonstrate understanding.
Dual enrollment as transition: students are moving from K-12 toward higher education and the workforce. The enduring goal is not compliance while watched; it is the judgment to use powerful tools responsibly after the class ends.
Minors, records, and tool access
Protect students before requiring an AI tool
A public consumer account is not automatically an approved educational environment. Dual-enrollment students are often minors, but age alone does not answer every legal or contractual question.
Practical data rule
Do not enter sensitive, restricted, confidential, or personally identifiable information into an AI system unless the applicable institution has approved that specific use and environment.
Use minimum necessary information and institution-managed access when provided.
FERPA context
FERPA applies to education records maintained by covered agencies and institutions and defines personally identifiable information broadly. A student attending a postsecondary institution is an “eligible student” under FERPA even if under 18, which can affect record rights and family communication.
Age and account terms
Check the product’s current minimum age, account terms, school or parent consent process where applicable, and whether the proposed account type is permitted. COPPA specifically addresses operators collecting personal information from children under 13; it is not a universal rule for every minor.
Before the college requires an AI product
- Minimum age and account requirements
- Consent issues where applicable
- Institutional license and privacy/security review
- Data retention, use, sharing, and training settings
- Accessibility and accommodation process
- Cost and availability to every student
- District device, network, and filtering compatibility
- Alternative access when required
- Technical support at the high-school site
- Clear plan if the tool or terms change
Resolve before the student acts
When K-12 and college rules conflict
Use a documented, issue-specific resolution path.
Identify
State the exact conflict, affected course or assignment, tool, timing, and students.
Pause
Do not ask the student to bypass controls or choose which institution to disobey.
Notify
Contact the Instructor of Record, postsecondary dual-enrollment office, district/school coordinator, and other designated authority.
Classify
Is the issue academic standards, technology access, privacy/security, conduct, accessibility, cost, or legal/compliance?
Resolve and document
Identify the appropriate authority, agree on an accessible path, record the decision, owner, and effective period.
Communicate and improve
Give the student one clear instruction and update future course materials, onboarding, or agreements if the issue is systemic.
Copy, adapt, and approve locally
Course and assignment templates
These are model communication tools, not legal language or mandatory policy.
Sample course AI statement
Artificial intelligence expectations may vary by assignment. Follow each assignment's AI instructions and ask before using AI when the boundary is unclear. Unauthorized assistance may violate applicable academic-integrity requirements. When AI is authorized, you remain responsible for the accuracy, sources, calculations, code, originality, and final work you submit. Do not upload protected, confidential, restricted, or another person's information into an unapproved tool. Disclosure or citation requirements will be stated when applicable. Some activities may intentionally use approved AI to build critical, responsible AI skills.
AI Use for This Assignment
Status: □ Required □ Permitted □ Limited □ Not permitted
You may: [Specify tools and allowed steps.]
You may not: [Specify restricted steps, content, or data.]
Disclosure / citation: [State requirement and format.]
Verify: You are responsible for checking all AI-assisted content.
Learning objective: [Name the thinking or work the student must perform personally.]
Questions: Ask before using AI if you are unsure.
Make alignment operational
Orientation, preparation, agreements, and annual review
Student AI orientation
- What generative AI is and is not
- College integrity and assignment rules
- Hallucinations, source checking, calculations, and code
- Privacy, safe tool use, and accounts
- Disclosure versus citation
- Learning with AI rather than outsourcing learning
- Critical comparison and human judgment
- AI in college and the workforce
- Student reflection or acknowledgment
Montgomery College’s dual-enrollment fellowship offers one instructional example: short ethics instruction, guided use, student reflection, and a student-created commitment.
Faculty and facilitator preparation
- Institutional AI and integrity requirements
- Permission language and assignment design
- Privacy, approved tools, access, and accessibility
- AI limitations and verification
- Limits of detection and fair evidence review
- Process-based assessment without continuous surveillance
- Educational responses and required formal procedures
- Escalation and the transition to college expectations
- Modeling responsible professional use
Partnership / MOU operating considerations
Mississippi’s current manual already requires a board-approved agreement before student participation in a dual credit program. Partners should consider clarifying AI in the agreement, annual operating procedures, handbook, or onboarding materials:
- Academic policy and course-level authority
- Approved or restricted technology and data
- Training and student orientation
- Integrity process and escalation contacts
- Tool access, accessibility, and conflict handling
- How policy changes are communicated
This is a recommended coordination list, not a claim that Mississippi law specifically requires AI clauses.
Annual AI alignment check
- College and district AI policies
- Approved tools, privacy, security, age, and account terms
- Syllabus and assignment language
- Integrity and detection practices against current evidence
- Orientation and faculty/facilitator preparation
- Student literacy and equitable-access opportunities
- Contacts, escalation path, and major policy changes
Six realistic situations
What should happen?
College requires it; district blocks it
Do not tell the student to bypass the network. Pause the activity for affected students, route the conflict through designated contacts, confirm the academic goal and district concern, and provide an approved tool, configuration, location, or equivalent alternative before grading.
The assignment is unclear
The syllabus mentions AI generally, but the assignment does not. Clarify before use when possible. If a concern arises, examine whether a reasonable student could know the rule, teach the missing boundary, and improve future instructions before assuming misconduct.
College prohibits AI on an assessment
A broad K-12 permission does not authorize AI on a college assessment with a clear, stricter course rule. Reinforce the assignment boundary and explain that independent performance is being assessed.
A minor cannot create the account
Do not shift the problem to the student or family after launch. Check the vendor’s current terms, institutional approvals, consent process where applicable, and alternatives; revise the activity or provide authorized access before it affects a grade.
A facilitator suspects misuse
Review the clarity of the rule, substantive evidence, and natural process artifacts. Talk with the student about the work and any AI use. Distinguish misunderstanding from a supported policy concern, then use the postsecondary institution’s established process when required.
Responsible AI-assisted learning
The assignment permits brainstorming and feedback. The student develops questions with AI, independently researches and writes, opens and checks every source, rejects inaccurate suggestions, discloses the permitted assistance, and can explain every conclusion. That is visible, accountable learning.
Continue with MAIN
Related Mississippi AI resources
Use this guide to align the dual-enrollment setting, then go deeper with the resource that owns the broader topic.
Higher Education policy template
Adapt governance, teaching, integrity, data, procurement, accessibility, and oversight language.
K-12 policy template
Plan age-appropriate, safe, responsible district AI use and local governance.
Mississippi institutional directory
Review public AI guidance from Mississippi colleges and universities; policies vary by institution.
Faculty AI Playbook
Use practical course rules, assessment design, student-data, accessibility, and human-review workflows.
Student AI Playbook
Help students study, research, verify, protect information, disclose assistance, and own their work.
Courses and practical skills
Build foundational literacy through MAIN’s free courses, student and educator resources, and prompting tips.
Policy and prompting hubs
Browse MAIN’s full collections when a partner needs a different sector template or role-specific practice guide.
Student and educator hubs
Find role-specific learning and support resources.
Statewide AI readiness
Connect dual enrollment with Mississippi’s shared AI literacy and workforce-readiness direction.
Practical questions
Frequently asked questions
Whose AI policy applies in a dual-enrollment class?
The postsecondary institution generally sets academic requirements for the college course. The K-12 partner may separately control local devices, networks, approved applications, accounts, privacy, and conduct. The current partnership agreement and applicable policies determine the specific answer; resolve conflicts before asking the student to act.
Can a high school create different AI rules for a college course?
A district can set rules for its technology environment, privacy, safety, and local conduct, but should not independently weaken or rewrite the academic requirements of the college course. If a district rule creates conflict, the partners should resolve it through the designated process.
Can one instructor allow AI while another does not?
Yes, when institutional and department policy permits instructor or assignment-level discretion. Different learning objectives may justify different rules. The important requirement is timely, assignment-specific clarity.
Is all AI use considered cheating?
No. AI misuse may become misconduct when it violates an applicable institutional policy, course rule, assignment instruction, or requirement about authorship, disclosure, citation, or misrepresentation. Authorized learning use is different from unauthorized assistance.
Can students use AI to study when it is prohibited on an assignment?
Possibly, but they should not assume so. A prohibition may apply only to the submitted task or may be broader. Ask whether AI can be used for explanations or practice without viewing or transforming the restricted assessment.
What are productive student uses of AI?
When authorized: explanations, practice questions, study support using the student’s own notes, brainstorming, research keywords, feedback, code debugging, language practice, comparison, and interview rehearsal. The student should verify outputs and keep their own reasoning central.
What happens when college and district technology rules conflict?
Do not tell the student to bypass controls. Identify whether the issue is academic, technical, privacy, security, accessibility, or compliance; notify both designated contacts; document an approved path or equivalent alternative; and communicate one clear answer.
Can students be required to create an AI account?
Only after partners confirm current age and account terms, consent issues where applicable, institutional approval, privacy/security, accessibility, cost, equitable availability, and district compatibility. Resolve barriers before grading the activity.
What if the student is under the tool’s minimum age?
The student should not misstate their age or bypass the product’s terms. The partners must select an approved alternative, institution-managed pathway, or revised activity that the student can legitimately access.
Should parents or families be notified about AI tools?
Follow applicable institutional, district, vendor, privacy, and consent requirements. Even when formal consent is not required, clear family communication about the purpose, data, account, cost, and support can prevent confusion. Remember that FERPA rights at the postsecondary institution may belong to the attending student even if under 18.
Should students disclose or cite AI use?
Follow the instructor, assignment, discipline, citation style, and institutional requirements. Disclosure explains how AI contributed; citation or attribution credits a source or tool. They may overlap, but this guide creates no universal format.
Can an AI detector prove a student cheated?
No. Current research shows uneven, changing performance, and a detector cannot determine authorization or misconduct. A score should not be standalone evidence for an adverse decision. Review the expectation, work, learning process, conversation, and applicable procedure.
Who handles suspected academic misconduct?
The postsecondary institution’s applicable academic-integrity policy and designated officials typically govern a concern in the college course, with K-12 coordination where its conduct rules or agreement also apply. The facilitator should use the defined reporting path rather than impose an independent penalty.
What belongs in the syllabus?
Course-level status, how assignment rules may differ, approved tools, prohibited data, responsibility for verification, disclosure or citation expectations, consequences under existing policy, and where to ask questions. Repeat the operative boundary on each important assignment.
Should dual-enrollment agreements address AI?
Partners should consider addressing AI in the agreement, annual operating procedures, faculty handbook, or onboarding materials. Clarify academic authority, technology and data, training, orientation, tool access, integrity processes, escalation, and policy-change communication. This is recommended planning, not newly claimed statutory language.
How often should AI guidance be reviewed?
Before each academic year or partnership cycle and after material policy, product, privacy, security, age/account, or evidence changes. A named owner and revision date make the review usable.
How does AI literacy support college and workforce readiness?
It teaches students to interpret rules, protect information, verify claims, disclose assistance, preserve their own thinking, recognize limitations, and use AI with accountable human judgment—skills that transfer beyond one course or product.
Authoritative foundation
Sources and references
Last source review: August 2026. Institutional practices below are examples, not Mississippi or national mandates. External links open in a new tab.
Mississippi
- Mississippi Institutions of Higher Learning: Dual Enrollment, including Mississippi Code § 37-15-38 and the current manual link.
- Mississippi Community College Board: Dual Enrollment and Dual Credit.
- Procedures Manual for Dual Enrollment and Accelerated Programs, Fall 2024, the edition linked as current by IHL and MCCB on the review date.
- Mississippi Department of Education: College and Career Readiness.
- MAIN: AI Guidance from Mississippi Institutions, documenting that local institutional guidance varies.
External institutional examples
- Southeastern Louisiana University 2026-2027 Dual Enrollment Handbook and 2026 faculty memo: Instructor of Record/facilitator roles and course-specific AI expectations.
- Pima Community College Dual Enrollment Student Handbook: college standards, AI misuse, and potentially overlapping conduct requirements.
- Alvin Community College 2025-2026 Dual Enrollment Student Handbook: one permission-based institutional model, accessed through its current 2026-2027 forms page.
- Maricopa Unified School District AI Guidance: AP and dual-enrollment students follow applicable external academic requirements.
- Montgomery College eFaculty Fellowship for Dual Enrollment: ethics instruction, guided practice, commitment, and reflection.
Privacy and minors
- U.S. Department of Education Student Privacy Policy Office: FERPA, including education records, personally identifiable information, and eligible-student definitions.
- SPPO: FERPA rights for a student under 18 enrolled in both high school and college, explaining the different record-rights context across the two institutions.
- Personally Identifiable Information for Education Records.
- Federal Trade Commission: Complying with COPPA FAQs.
AI detection evidence
- Van Vlasselaer, Van Droogenbroeck & Spruyt (2026), Who wrote this? One tool performed strongly in the study’s controlled dataset; tools should provide initial flags, not sole high-stakes evidence.
- Elkhatat et al. (2026), Evaluating the accuracy and reliability of AI content detectors. Authentic EFL writing raised reliability, generalizability, and equity concerns for high-stakes judgments.
- Turnitin: Using the AI Writing Report. Vendor documentation warns against sole-basis adverse action and describes false-positive limits.
Educational and planning resource: This guide does not replace applicable law, state requirements, institutional or district policy, academic-integrity procedures, partnership agreements, accessibility processes, product terms, or legal counsel where needed. MAIN does not set institutional academic policy.
Clear boundaries and responsible AI use can coexist
Align the two systems, teach students how to judge, and keep the college-level learning visible.