AI as a Professor’s Strategic Partner: A Dual-Course Implementation Model for Culturally and Linguistically Responsive Teacher Preparation

Abstract Book of the 11th International Conference on Future of Teaching and Education

Year: 2026

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AI as a Professor’s Strategic Partner: A Dual-Course Implementation Model for Culturally and Linguistically Responsive Teacher Preparation

Prof. Dr. Eulises Avellaneda

ABSTRACT:

This practice-oriented presentation details an innovative dual-course implementation in which artificial intelligence (AI) served as a strategic co-designer in reimagining foundational teacher preparation at an urban university. Across two courses—the 8-week Technology for Teachers and the 16-week Educational Psychology—the instructor collaborated with generative AI tools (ChatGPT, NotebookLM) to co-author and refine five core instructional elements:
(1) Course learning goals and syllabi, intentionally aligned with CAEP, InTASC, and OSSE standards;
(2) Culturally and linguistically responsive module objectives that center asset-based frameworks (e.g., “emergent bilinguals”);
(3) Interactive learning activities designed to deepen engagement with content through differentiated, multimodal approaches;
(4) Standards-aligned assessment activities measuring candidate growth in pedagogical knowledge, content understanding, and linguistic competence; and
(5) Comprehensive, personalized feedback addressing three key domains: content mastery, academic writing, and professional use of English—particularly supporting candidates developing English proficiency.
All AI-generated outputs were critically reviewed, adapted, and validated by the professor to ensure developmental appropriateness for early childhood and elementary education contexts (ages 3–8) and alignment with equity-centered pedagogy. Additionally, teacher candidates were encouraged to use AI as a co-editor to review their work and co-design components of assessments, fostering reflective engagement with AI’s potential and limits.
The session will showcase concrete examples of this human-AI collaboration—including prompt engineering strategies, quality control protocols, and ethical guardrails—and provide attendees with adaptable templates to implement this model in their own contexts. This approach demonstrates how faculty can ethically leverage AI to strengthen—not supplant—professional judgment while preparing future teachers for diverse, linguistically rich classrooms.

Keywords: AI In Teacher Education; Culturally and Linguistically Responsive Teaching; Accreditation-Aligned Design; Human-AI Collaboration; Prompt Engineering; Teacher Preparation; Educational Technology