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EDCO 1A — Start: College and Career Readiness [pdf]

This dual-enrollment course introduces high school students to college and career readiness through self-assessment, reflection, and structured exploration of educational and career pathways. Developed as part of the GEAR UP Dual Enrollment Career Readiness Course Series, the course helps students identify their interests, strengths, skills, and values; investigate careers and postsecondary options; and develop academic and career goals.

Across the course, students consider several guiding questions: Who am I as a learner and future professional? What educational and career pathways align with my interests, strengths, and values? How can I use available resources—including AI—without giving up my own thinking, voice, and decision making?

The course is organized around six interconnected units that move students from self-exploration toward academic and career planning:

Unit 1 — College and Career Readiness Foundations: Students are introduced to college and career planning and establish their CaliforniaColleges.edu profiles. Through a skills inventory and initial reflection, they consider what college and career readiness means, identify skills they already possess, and identify areas they want to strengthen as they prepare for life after high school.

Unit 2 — Interests, Strengths, and Values: Students use interest and values assessments to develop greater awareness of the personal characteristics that may influence their educational and career choices. They also engage in an AI-supported reflection in which AI generates questions based on their assessment results. Students respond to those questions in their own voice and explicitly consider where AI-generated suggestions were useful, where they disagreed, and what decisions remained their own.

Unit 3 — Career Exploration: Students investigate potential career pathways connected to their emerging interests. They research and compare careers in relation to education requirements, skills, salary, lifestyle, and personal fit, moving from general career awareness toward a more considered understanding of the opportunities and challenges associated with different professional pathways.

Unit 4 — College Majors and Postsecondary Pathways: Students connect career possibilities to educational pathways by exploring college majors and postsecondary institutions. They consider multiple routes after high school—including two-year and four-year institutions, career and technical education, and apprenticeships—and reflect on relationships among majors, skill development, and possible careers.

Unit 5 — Academic Planning and Readiness: Students turn their emerging career and postsecondary interests into an academic plan. They review high school coursework and graduation and A–G requirements and consider the transition from high school to postsecondary education. AI is used as a planning resource to suggest possible academic and career pathways, but students are required to evaluate those suggestions against their own interests, values, circumstances, and goals and revise them accordingly.

Unit 6 — Academic and Career Planning and Reflection: Students synthesize their learning through an Academic and Career Planning Portfolio and GEAR UP Student Achievement Plan. The portfolio brings together their personal profile, career exploration, college majors and pathways, academic plan, short- and long-term goals, and reflections on AI use. Students conclude by identifying what they have learned and considering next steps for their academic and career development.

Across the six units, AI is positioned as a learning aid rather than a substitute for learning. Students may use approved AI tools for purposes such as brainstorming, clarifying concepts, generating reflection questions, exploring pathways, and receiving feedback, while submitted work must demonstrate their own thinking and academic voice. The course therefore introduces AI literacy within the larger process of college and career readiness, asking students not simply to obtain AI-generated recommendations but to evaluate, revise, question, and make their own decisions about their futures.

EDCO 298 — Special Studies in Counselor Education [pdf]

This graduate-level Counselor Education course supports master’s degree candidates as they complete the culminating Plan B project begun in EDCO 221: Research in Counselor Education. Candidates complete either a data-collection study or a counseling prevention/intervention proposal while developing their capacities to use research, professional literature, assessment methodologies, and ethical judgment to contribute to counseling and guidance practice.

Across the course, candidates consider several guiding questions: How can research inform professional counseling practice? How can evidence be interpreted and communicated responsibly? What forms of analysis, professional judgment, and authorship must remain the responsibility of the counselor-researcher when AI tools are available?

The course progresses through several interconnected phases of research development and completion:

Unit 1 — Research Foundations, Ethical Inquiry, and Responsible AI Use: Candidates revisit the foundations established in EDCO 221 and refine Chapters I–III of their projects: Introduction, Literature Review, and Methodology. They examine exemplars, clarify the personal, academic, and professional motivations underlying their research topics, and consider ethical uses of AI in counseling research. Candidates engage with the Five Pillars of Ethical AI Use for Teaching and Learning, with particular attention to Accuracy and Authenticity, while completing initial reflection without AI assistance to establish their own questions and perspectives.

Unit 2 — Results or Counseling Prevention/Intervention Proposal: Candidates develop Chapter IV according to the nature of their Plan B project. Those conducting research develop and report results, while those pursuing the alternative pathway construct a counseling prevention/intervention proposal. Candidates conducting data-collection studies must address applicable human-subjects requirements before beginning data collection.

Unit 3 — Summary, Conclusions, and Recommendations: Candidates develop Chapter V by interpreting and synthesizing their project. They connect findings or proposed interventions back to the theoretical and empirical foundations of the study and consider implications and recommendations for counseling and guidance practice.

Unit 4 — Completing the Professional Research Document: Candidates assemble the components of the complete Plan B project, including the title and signature pages, abstract, table of contents, Chapters I–V, references, and applicable appendices. Particular attention is given to APA conventions, professional writing, coherent presentation of evidence, and the relationship among the different components of the project.

Unit 5 — Feedback, Revision, and Finalization: Candidates submit a complete project draft and engage in iterative revision based on instructor feedback. Individual consultation supports candidates as they strengthen their analysis, writing, organization, and presentation before submitting the final project for approval.

Unit 6 — Ethical AI Use, Reflection, and Professional Judgment: AI use is situated within the ethical responsibilities of counseling professionals. Candidates may use AI only when permitted and for defined learning supports such as brainstorming, conceptual clarification, or editing for clarity and grammar. AI may not replace original analysis, reflection, or clinical judgment, and confidential, identifiable, or sensitive information concerning clients, students, families, schools, or field placements may not be entered into AI systems. When AI is used, candidates disclose the tool and purpose through an AI Use Statement and remain responsible for verifying the accuracy and appropriateness of AI-assisted material.

Across these units, candidates develop the culminating project through a combination of independent inquiry, instructor consultation, feedback, revision, and reflection. The course connects research methodology to professional practice while emphasizing that ethical research in counselor education requires candidates to maintain responsibility for their evidence, interpretations, professional voice, and decisions. The completed five-chapter Plan B project serves as evidence of their ability to conduct or design research-informed work that contributes to the counseling and guidance field.

EDTE 224 — Psychological Foundations of Education [pdf]

This preservice teacher education course examines constructivist, sociocultural, behaviorist, and cognitive theories of learning and development and their applications to teaching in secondary school settings. Across the semester, candidates return to three overarching questions: Who is a learner? How does the learner learn? Why did learning theory evolve to address learning and development in K–12 settings?

The course is organized around four major units that move from foundational theories of learning toward emerging questions about learning and development in AI-mediated environments:

Unit 1 — Constructivist and Sociocultural Theories of Learning: Candidates examine learners as active meaning makers, with particular attention to prior knowledge, schema, novice and expert learning, developmental progression, the Zone of Proximal Development (ZPD), and scaffolding. The unit asks candidates to distinguish learning from development and consider how social interaction and participation shape growth.

Unit 2 — Behaviorist Theories of Learning: Candidates investigate learning through conditioning and examine how stimulus and response, repetition, reinforcement, reward and punishment, praise and blame, explicit routines, and direct instruction can shape learner behavior. The unit also considers the continuing presence of behaviorist principles in contemporary K–12 classrooms.

Unit 3 — Cognitivist Theories of Learning: Candidates examine the learner as an information processor, focusing on attention, working and long-term memory, cognitive load, rehearsal, organization, retrieval, practice, prior knowledge, knowledge types, and transfer. Candidates consider how instructional design can either support or overwhelm learners’ cognitive processing.

Unit 4 — Future Directions of Learning in AI-Mediated Spaces: Candidates revisit the fundamental question of who—or what—counts as a learner in the 21st century. They compare human and machine learning and investigate productive struggle, interactive and interpassive learning, AI-assisted learning modalities, and the evidence educators need to determine whether students are actually learning, developing, and making progress.

Across the four units, candidates analyze and evaluate lessons from each theoretical perspective before completing a culminating lesson-design exhibition that brings multiple theories together. Guided by the Five Pillars of Ethical AI Use for Teaching and Learning—with a focus on Accuracy, Agency, Accessibility, Assessment, and Authenticity—the culminating work asks candidates to consider who or what is doing the cognitive work, how productive struggle can be preserved, and when AI supports learning rather than substitutes for the intellectual work and professional judgment learning requires.

EDTE 260 — Critical Perspectives on Schooling for a Pluralist Democracy [pdf]

This preservice teacher education course examines classroom learning environments as historically situated, socially experienced, deliberately created, and increasingly shaped by emerging technologies. Rather than beginning with prescriptions for what a good classroom should look like, candidates first learn to observe and interpret schooling before developing and articulating their own professional commitments. Across the course, candidates return to several guiding questions: What makes a classroom learning environment good, positive, and valuable? How do teachers deliberately create and sustain the conditions for learning? What should remain distinctly human in classroom communities as AI becomes increasingly present in teaching and learning? 

The course is organized around six themes:

Theme 1 — Seeing Schooling: Candidates learn to look closely at schools and classrooms, examining how routines, relationships, spaces, language, expectations, and interactions shape students’ experiences. Frederick Wiseman’s documentary High School provides an initial case for noticing schooling before judging or explaining it.

Theme 2 — Interpreting Schooling: Candidates place their observations within historical, cultural, and research-based perspectives. Using High School and High School II alongside educational scholarship, they consider how different perspectives change what educators think they are seeing, whose perspectives are represented or absent, and what earlier eras of schooling can teach educators today.

Theme 3 — Imagining Better Learning Environments: Candidates move from description and interpretation toward questions of educational value: What makes a classroom learning environment good, positive, and valuable? They consider how teachers, students, parents and guardians, and other community members might answer this question differently and identify qualities they hope to cultivate—or avoid—in their own classrooms.

Theme 4 — Examining How Teachers Create Learning Environments: Candidates turn from aspirations to observable teacher practice. Through classroom video cases, they investigate how teachers use formative assessment moves, priming, routines, norms, relationships, interactions, and other practices to establish conditions for participation and learning. They identify practices they might borrow, adapt, or reject in their own teaching.

Theme 5 — Articulating One’s Own Practice: Candidates begin making their professional commitments explicit. They consider what they value as teachers and how those values would become visible through classroom practices and professional artifacts, with particular attention to Authenticity and Identity, Accessibility and Belonging, and Agency and Voice.

Theme 6 — Considering Learning Environments in the Age of AI: Candidates reconsider those commitments as AI enters the classroom environment. They investigate how AI-mediated tools may alter authenticity, identity, accessibility, belonging, agency, voice, authorship, participation, and relationships. A central question becomes what should remain distinctly human in the creation and maintenance of classroom communities and how teachers should exercise professional judgment about when, how, and under what conditions AI supports worthwhile learning.

The culminating exhibition, Articulating My Practice in the Age of AI, asks candidates to develop a personal statement on AI use in their classroom and a set of professional artifacts that make their commitments and concerns visible. Candidates explain what they value, how those values would appear in practice, and what forms of human judgment and relationship they intend to preserve as educational technologies continue to evolve.

EDTE 282 — Assessment and Evaluation in Secondary Schools [pdf]

This preservice teacher education course examines assessment of and for student learning as both an art and a science. Candidates study research-based approaches to classroom assessment and evaluation that make student learning visible and provide evidence that teachers and students can use to make decisions about what to do next. The course treats classroom assessment as a process of principled decision making rather than simply testing, grading, or scoring. Candidates also examine how emerging technologies, including generative AI, can be used productively in assessment design while developing the professional judgment needed to evaluate their contributions and limitations.

The course is grounded in the and three recurring questions: What are we aiming to assess? How will we observe students demonstrating what they know and can do? How will we interpret the evidence we see?

Candidates investigate these questions through six interconnected units of study.

Unit 1 — Learning Goals, Tasks, and Cycles of Feedback: Candidates begin by examining relationships among clearly defined learning goals, rich assessment tasks, evidence of student learning, and cycles of formative feedback. They consider how assessment can create opportunities for students to receive feedback, revise their work, and continue making progress. Candidates also experiment with AI as a resource for generating and refining learning goals and reflect on the decisions required to evaluate and improve AI-generated suggestions.

Unit 2 — Directionality of Formative Feedback: Candidates investigate how feedback moves in different directions within a learning environment, including teacher-driven, peer-to-peer, and self-driven feedback. Particular attention is given to developing students’ capacities to participate in assessment rather than positioning them only as recipients of teacher judgments. The emergence of AI-generated feedback provides an additional context for considering who generates feedback, who interprets it, and how it enters cycles of improvement.

Unit 3 — Configurations and Modalities of Feedback: Candidates examine how formative feedback operates with whole classes, small groups, and individual learners, as well as through written, spoken, and nonverbal modalities. They consider how different configurations and modes of feedback create different opportunities for teachers to notice, interpret, and respond to student thinking and how technology may alter the production and delivery of feedback.

Unit 4 — Formative Assessment Moves: Candidates develop expertise with seven high-leverage FA Moves—Priming, Posing, Pausing, Probing, Bouncing, Tagging, and Binning–by planning, engaging, and reflecting on mini-lesson they teach to the whole class. Testing out these research-based practices helps pre-service teachers to practice eliciting and interpreting student thinking, engage learners in classroom assessment processes, and make responsive instructional decisions during teaching and learning.

Unit 5 — Designing Classroom Assessment Tools: Candidates apply assessment principles by constructing a connected set of tools around a significant classroom learning goal. They define a learning goal, design a rich multi-day task, construct an analytic rubric, and develop progress guides that students can use for peer and self-feedback and that teachers can use to determine productive next instructional steps. AI is incorporated into portions of this design process—including the development of learning goals and analytic rubrics—followed by structured reflection on its use.

Unit 6 — AI-Enriched Assessment and Feedback: Candidates bring these strands together by explicitly examining AI-enriched tools and processes for generating and refining rich tasks, rubrics, progress guides, and other classroom assessment resources. Rather than treating AI-generated materials as finished assessment products, candidates evaluate, adapt, and reflect on them using the assessment principles developed throughout the course. The emphasis remains on the teacher’s professional judgment in determining whether AI-supported assessment tools produce useful evidence and feedback for classroom purposes.

Across the six units, candidates learn to connect formal and informal assessment, formative feedback, instructional decision making, and emerging AI-supported practices. The course culminates in the design of a coherent assessment system for a major classroom assignment, requiring candidates to connect learning goals, rich tasks, criteria, feedback, revision opportunities, and progress indicators while documenting and reflecting on their use of AI during the assessment-design process.