AI Course Designators

TXST's AI Course Designator Framework gives faculty a structured approach to transparently communicate how artificial intelligence is integrated into courses. The framework provides clarity for students, supports academic integrity, and promotes equitable access to AI tools.

Our Initiative

This framework draws on nationally recognized models for comprehensive AI education integration, adapting proven approaches to Texas State University's distinctive mission of "excellence, discovery, and innovation."

Leading institutions have demonstrated how infusing AI literacy across all majors and colleges can drive meaningful institutional transformation. Texas State can leverage these validated strategies to establish five distinct AI course designators that reflect both educational best practices and TXST's relentless pursuit of academic excellence.

woman looking at a laptop

MISSION ALIGNMENT

Each designator connects to Texas State's mission of "excellence, discovery, and innovation" and its commitment to "meaningful student engagement built on active involvement, accessibility, and intentional educational experiences."

STRATEGIC VISION

The initiative aligns with Texas State's "Hopes & Aspirations High" vision and 2023–2029 Strategic Plan goals, including Carnegie R1 status aspirations.

COMMUNITY VALUES

It reflects the university's commitment to "creating a sense of belonging across unique communities, identities, ideas, and contributions" and ensuring strengths benefit those served locally and globally.

TXST University AI Designators

Expand FAQ List

  • Timeline

  • AI Course Designators

    • Course designators are specialized labels that identify courses containing substantial content in specific focus areas, offering enhanced visibility and clarity for students, advisors, and stakeholders about the knowledge and skills emphasized within the curriculum. They serve multiple purposes: helping students find courses matching their academic and career interests, assisting advisors in guiding relevant learning paths, supporting employers in understanding graduate skills, and enabling institutions to track and evaluate learning outcomes.

      At Texas State University, AI course designators are optional. These are labels that faculty and departments can add to existing courses to emphasize important AI-related content. It is the prerogative of the department and the College to determine the alignment of the AI content and the designator in the respective course sections.

    • Banner CodeTXST DesignatorFocus AreaShort Focus Area (Registrar)
      AIEUAI-EngageApplication-focused courses that teach practical use of AI tools and platformsPractical Implementation
      AIDKAI-DiscoverFoundational courses that provide basic understanding of AI functions and conceptsFoundational Knowledge
      AICBAI-CreateTechnical courses focused on designing and developing AI systems using higher-order thinkingTechnical Development
      AIREAI-ReflectEthics and policy courses examining AI's societal implications and responsible developmentResponsible AI and Society
      AISEAI-SupportSupporting courses that develop foundational skills (programming, statistics) that enable AI learningEnabling Skills and Knowledge

  • Approval Process

    • At Texas State University, AI course designators are optional. The approval process ensures that AI course designators are applied consistently, transparently, and in alignment with academic standards. It balances faculty innovation with departmental, college, and university oversight, while safeguarding curricular integrity, accreditation requirements, and accurate representation in the catalog and schedule.

    • 1
      Faculty Initiation
      • Faculty identify AI-related content existing in or that can be embedded in the course.
      • Faculty complete the AI Designation Request Form (expected in the CIM system Fall 2026) or its alternative, which will include course details, designation type, description, and AI-related SLOs.

      1. a. Department Curriculum Committee (or assigned faculty committee) 

      • Evaluate the proposed designation and makes a recommendation to the department chair. 
      2
      Department Chair Review
      • Reviews for accuracy, alignment with course objectives, and departmental priorities.
      • Approves and forwards the request to the Dean.

      2. a. College Curriculum Committee (or assigned faculty committee) 

      • Review the request and assess the designation with college wide priorities, cross- departmental AI integration, etc. 
      • Submit a recommendation to the Dean. 
      3
      Dean's Review
      • Ensures consistency with college-level priorities, balance of offerings, and resource feasibility.
      • Approves and forwards the request to the Vice Provost for Academic Innovation (VPAI).
      4
      Vice Provost for Academic Innovation (VPAI)
      • Provides university-level oversight for the initiative.
      • Delegates review and validation to the Associate Vice Provost for Curriculum and Academic Programs (AVPCAP).
      5
      AVPCAP (on behalf of VPAI)
      • Validates alignment with official AI designation definitions.
      • Confirms measurable AI-related SLOs and compliance with accreditation/assessment and university standards.
      • Coordinates approval flow with the Registrar's Office.
      • Notifies the UCC of all approved AI course designations, including the specific sections. 
      6
      Registrar's Office
      • Tags approved AI designators in the catalog, Banner, and schedule of classes.
      • Ensures section-specific distinctions are reflected in the course schedule (e.g., one section designated, another not).
      • Notifies advisors and maintains the official record of AI-designated courses.
      7
      Students
      • See AI designations in the catalog and schedule.
      • Review designation language and SLOs in syllabi.
      • Enroll in AI-focused courses as desired (optional).

  • Assessment

    • The annual assessment of AI course designations by departments in collaboration with the Office of Program Accreditation and Assessment (OPAA) is crucial for maintaining academic integrity, ensuring student learning outcomes, and demonstrating institutional accountability. This collaborative process confirms that courses continue to meet their specified learning outcomes, provides data for curriculum improvement, and ensures alignment with industry standards and technological advances. Without consistent assessment collaboration, AI course designators risk turning into mere symbolic labels rather than meaningful indicators of student skills.

    • Assessment TypeOverviewPilot Status
      AI Learning Outcome TrackerFoundational tool integrating pre-course, mid-course, and end-of-course evaluations into existing classroom activities to measure student growth in AI competencies specific to each designator type.Recommended
      Simplified Portfolio AssessmentLeverages existing assignments as evidence of AI learning, requiring faculty to collect only one artifact per student demonstrating achievement of designator-specific outcomes.Required
      Department AI Assessment SummaryAggregates data annually to provide administrators with a comprehensive view of AI education effectiveness, identifying trends, successes, and areas needing support.Recommended
      Quick AI Learning Pulse SurveyRapid 2-minute feedback mechanism for mid-semester and end-of-semester student input on AI content understanding, application confidence, and course integration quality.Required
      Faculty Reflection DashboardStructured 10-minute self-assessment for instructors to evaluate AI content delivery effectiveness, student achievement rates, and professional development needs.Recommended
      Program-Level AI Integration AssessmentEnables academic programs to analyze AI pathway completion, designator distribution, graduate outcomes, and industry feedback to inform strategic curriculum decisions.Recommended

       

      Collectively, these assessments create a multi-layered ecosystem capturing individual student learning, course effectiveness, department performance, and program impact — ensuring that AI course designators maintain their academic rigor and relevance in preparing students for an AI-enhanced future.

       

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