This presentation focuses on the results of an implementation evaluation of a college-wide teacher performance assessment (TPA) framework which was developed collaboratively by the College's Assessment Taskforce in 2022-2023. The assessment was implemented in 13 initial and dual certification programs in 2023-2026. The TPA framework was intentionally designed to be adapted to varied programmatic contexts, to emphasize local knowledge and expertise, to prevent a narrowing of teacher education curriculum, and to encourage innovation. The three-year implementation study consisted of investigation of implementation fidelity (based on faculty interviews and reviews of program materials), participant satisfaction (based on completer survey), and educational effectiveness (based on the analysis of assessment data). We briefly describe the results of these three-part investigation and discuss the TPA framework potential to serve multiple purposes of accountability, improvement, and innovation.
Drawing on an 18-month ethnography of a public high school's journey toward LGBTQ+ inclusivity, this presentation examines how schools become more inclusive and explores implications for teacher preparation. Findings highlight the importance of educator decision-making, student voice, professional learning, and practical strategies for preparing teachers for inclusive practice.
Rapid developments in digital technologies have significantly transformed teaching and learning processes, increasing expectations for teachers to integrate innovative tools into classroom practice. Virtual Reality (VR) has gained attention in education due to its immersive, interactive, and experience-based learning opportunities. VR has the potential to enhance students’ engagement, motivation, and conceptual understanding.
As schools of education increasingly shift responsibility for preservice teacher (PST) preparation to school-based settings, the role of mentor teachers (MTs) has become central to the development of new science educators. Two related but distinct lines of inquiry address this shift. One examines reciprocity within the MT/PST relationship — the dynamic through which mentors and mentees mutually exchange support, knowledge, and skills (Cropanzano & Mitchell, 2005; Homans, 1971). Although no legal mandates require MT preparation, research consistently demonstrates that mentors who receive structured training report greater confidence and effectiveness, with corresponding benefits for PST learning outcomes (Parker et al., 2021; Ambrisotti, 2014; Hudson & Hudson, 2016). Without such support, preparation varies widely, creating inconsistencies that affect both mentor effectiveness and PST development.The other examines the role a structured community of practice consisting of mentors, PSTs, and master teachers plays in PST’s ability to engage students in science practices. The integrated analysis showed that strong, collaborative mentor relationships were a primary driver of gains in pedagogical content knowledge (PCK) specific to science practices (PCK-SP). At the same time, PST agency strongly predicted growth across all outcomes. The CoP served as a critical support structure, acting as a “third space” and surrogate mentor that provided access to collective expertise and experiential learning. This study’s findings support a practice-informed model utilizing “three pillars” for PST growth founded on collaborative mentorship, PST agency, and community structures. By aligning these pillars, teacher preparation programs can better equip new educators to enact ambitious science teaching, ultimately advancing equitable science education.
As artificial intelligence becomes increasingly integrated into educational practice, educator preparation programs face growing pressure to determine how best to prepare future teachers for AI-enabled classrooms. At the same time, teacher candidates are expressing a desire for more learning opportunities related to AI and its role in teaching and learning. This presentation describes the design and implementation of an AI literacy module within a graduate teacher preparation program that was developed in response to candidate demand and grounded in the concept of professional judgment. Rather than focusing solely on tool use, the module engaged candidates in critically evaluating AI-generated content, considering ethical implications, identifying limitations and biases, and making instructional decisions that prioritize learner needs. The session will share the rationale for integrating AI literacy into teacher education, highlight key design principles and learning experiences, and discuss emerging lessons learned. Attendees will leave with practical ideas for incorporating AI literacy into educator preparation coursework while emphasizing professional judgment as an essential competency for future educators.
