Planning for differentiated processes in coursework offers a
powerful and practical entry point for institutionalizing formative assessment
because both approaches share a common foundation: Responsiveness to student
needs and a commitment to growth over time. Inherently, differentiated
processes offer varied pathways for students to engage with content, develop
skills, and demonstrate understanding. By embedding formative assessment
into these processes faculty can gather ongoing, meaningful evidence about how
students are progressing on different paths.
This can facilitate timely adjustments to teaching based on each student’s
zone
of proximal development (Vygotsky, 1978). This will reinforce a classroom
culture that values learning as a process rather than solely as an outcome. By
planning for differentiation, faculty are naturally positioned to build in
checkpoints that support personalized learning
through formative feedback.
Differentiated planning and processes typically include
scaffolding, tiered tasks, and provision of choice, which also creates the opportunity
to apply UDL 3.0 Guidelines and Considerations.
Each element of differentiation and application of UDL
Principles for Postsecondary Professional Development logically invite formative check-ins through progress
journals, draft submissions or peer feedback.
Thus, the opportunity for students to receive guidance tailored to their
selected process or challenge level, reinforces a system whereby formative
feedback is not just a side activity, but part of the course architecture. This
results in formative assessment processes feeling integrated and essential
rather than optional.
Differentiated planning acknowledges that students bring diverse
experiences, skills, and needs to the classroom. Formative
assessment complements this by providing students multiple low-stakes
chances to show progress and receive support. This intentional opportunity encourages
reflection, self-assessment, and student empowerment in the learning process. This further helps faculty recognize when
differentiation is either working or when it needs adjustment. Together, differentiated
planning and formative assessment practices normalize variability and support
inclusive teaching.
Proficiency with formative assessment strategies can be
instrumental in helping university faculty guide students toward authentic and
ethical use of generative
artificial intelligence (AI). Formative assessments often include reflective
activities. Journals, drafts with feedback, or self-assessments can
prompt students to think critically about how they are using AI tools, why they
are using them, and whether their usage aligns with course expectations and
academic integrity. This encourages transparency and ethical reflection in
their AI utilization.
Faculty can provide opportunities for responsible AI utilization
through low-stakes assessments or designing tasks that encourage exploration of
AI capabilities. By Integrating scaffolded discussions on bias,
accuracy, originality, and ownership faculty can provide feedback on both
content and process, including how students documented or integrated AI. These
formative strategies create space for instruction and correction before summative grading.
By frequently using formative assessments through progress checks,
drafts, or peer reviews, for example, faculty can monitor student thinking and
development over time, identify discrepancies in tone, style, or reasoning that
may suggest overreliance upon AI. Therefore, faculty can then
intervene early with feedback, support, or redirection.
Proficiency with formative assessment strategies helps
prevent academic dishonesty and guides students toward ethical
habits. Faculty can embed clear expectations and transparent modeling of
ethical AI utilization into formative tasks such as requiring students to annotate where and how AI tools were used, planning
for reflective writing on the role of AI in their developmental and editing
processes or co-constructing norms and policies around AI use with the
students. The result is formative assessment approaches becoming a platform for
building a shared understanding of acceptable practices.
Obviously, not all students will have the same familiarity or
comfort with AI. Employing formative assessment strategies can help faculty
identify students who need technical support or ethical guidance so faculty can
effectively differentiate instruction accordingly. Adjusting
course design or support resources to meet evolving needs is simply good
instruction that invites learners to optimize their human potential. This leads
to more equitable, intentional, and ethical integration of generative AI. Yes,
formative assessment strategies provide a feedback-rich, low-stakes environment
that is ideal for fostering responsible, reflective, and ethical engagement
with AI tools in higher education.
Institutions aiming to scale formative assessment practices should
consider adopting differentiation frameworks as UDL to anchor its course design. Such an adoption can highlight how formative
assessment fits seamlessly into differentiated planning and instructional
practice. It would be important to offer
professional development that synchronizes differentiated planning and formative
assessment practices as mutually reinforcing rather than as separate pedagogies.
This could reduce resistance and show instructors how to embed assessment in
ways that enhance rather than complicate course design.
Effective differentiated processes generate richer, more
individualized data through formative assessment. The synchronization makes it easier to track
student growth, document how instructional flexibility improves outcomes, and
build an institutional case for formative assessment as a system-level tool for
student success. When coursework embeds differentiated processes, formative
assessment becomes both a natural and necessary mechanism for monitoring varied
learning paths, supporting diverse learners, and ensuring instruction is
dynamic and equitable. Thus, alignment makes it much easier for institutions to
normalize and sustain formative assessment practices at scale.
The following activities were designed with
the assistance of ChatGPT (OpenAI, 2024), a generative AI tool. The first example is a potential starting
point for Higher Education faculty to use AI to effectively develop students’ ethical utilization
of Generative
Artificial Intelligence. The second example exhibits
how a teacher preparation course requiring application of Universal Design for
Learning (UDL) principles in lesson planning and instructional design can
further develop students’ ethical utilization of AI.
Formative Activity:
"AI in My Process" – Reflective Draft and Annotation
Objective: Help students
practice using generative AI as a thinking partner while developing
transparency and ethical awareness about its role in their academic work.
Part 1: Draft a Short Written
Assignment
Students complete an early
draft of a course-related assignment (e.g., an essay, research summary,
discussion post).
They are encouraged—but not
required—to use a generative AI tool (e.g., ChatGPT, Grammarly, Bard) as a
support resource (e.g., for brainstorming, outlining, or revising).
They must document and
annotate all AI use directly in the draft using comments or footnotes (e.g.,
"I used ChatGPT to generate three initial topic ideas" or "I
asked the AI to rephrase this paragraph more concisely").
Part 2: Reflective Memo (300–500
words)
Students submit a short
reflective memo addressing the following prompts:
Describe how you used (or
chose not to use) AI in the drafting process.
What did the AI contribute,
and how did you evaluate its reliability, originality, or usefulness?
How did you ensure that your
work remains your own, both intellectually and ethically?
Would you do anything
differently next time you use AI? Why or why not?
Part 3: Instructor Feedback
(Formative)
The instructor reviews the
draft and reflection, focusing on:
Alignment with course and
institutional academic integrity policies,
The thoughtfulness and
transparency of the student's process,
Feedback on how to improve
both the work and the ethical integration of AI tools.
Why This Works:
Encourages meta-awareness
and ethical decision-making.
Allows students to
experiment without penalty.
Helps faculty spot patterns
of misuse or misunderstanding early.
Builds a classroom culture
of open dialogue about technology and responsibility.
Formative Activity:
“Designing with UDL and AI” – Annotated Lesson Plan + Reflection
Course Context: Teacher
preparation course focused on applying Universal Design for Learning (UDL)
principles in lesson planning and instructional design.
Objective: Help teacher
candidates ethically and authentically explore how generative AI can assist in
developing inclusive lesson plans aligned with UDL principles, while reflecting
on its ethical and pedagogical implications.
Part 1: Draft a UDL-Enhanced
Lesson Plan
Students draft a short
lesson plan (or a portion of one) targeting a specific content area and grade
level. The plan must include:
At least one goal, one
method, and one assessment strategy explicitly aligned with UDL principles
(e.g., multiple means of engagement, representation, and action/expression).
Students are encouraged to
use a generative AI tool (e.g., ChatGPT, Curipod, MagicSchool.ai) to assist
with idea generation, scaffolding strategies, or accessible materials.
Annotation Instructions
Students must annotate the
lesson plan draft using comments (or footnotes) that answer:
Where did you use AI?
What did the AI generate?
How did you adapt or
evaluate the AI-generated content to align with UDL?
Why was this helpful—or not
helpful?
✅
Example Annotation:
"Used ChatGPT to
suggest ways to represent vocabulary visually; adapted one idea (image cards)
and rejected another (animated GIFs) due to age-appropriateness."
Part 2: Reflective Memo (300–500
words)
Prompt students to reflect
on:
How did using AI impact your
thinking or design process in relation to UDL?
How did you ensure the
lesson remains responsive to learner variability rather than
'one-size-fits-all'?
What ethical considerations
did you keep in mind while using AI in an educational planning context?
What are the implications of
AI-assisted planning for equity, accessibility, and teacher responsibility?
Part 3: Instructor Feedback
Provide low-stakes,
formative feedback focused on:
Appropriate integration of
UDL principles,
Ethical awareness of AI use
in educational contexts,
Clarity of student reasoning
in annotations and reflection.
Optional: Use a rubric that lightly
scores on transparency, UDL alignment, and ethical reflection (not AI
"accuracy").
✅
UDL Tie-In for Faculty
This activity itself models
UDL by offering:
Multiple means of
engagement: students explore real-world tools like AI.
Multiple means of action and
expression: students show learning via plans, annotations, and reflection.
Multiple means of
representation: encourages integrating varied learner needs through AI-assisted
supports.
To
cite:
Anderson, C.J. (April 30, 2025) Proficiency with formative
assessment strategies can help faculty
effectively develop students’ ethical
utilization of generative
artificial intelligence. [Web log post] Retrieved from http://www.ucan-cja.blogspot.com/
References:
Black, P., & Wiliam, D. (2006). Developing a theory of
formative assessment. In J. Gardner (Ed.), Assessment and learning (pp.
81–100). Sage.
CAST
(2024) UDL
3.0 Considerations
NWEA. (2016). 4 formative assessment practices that make a
difference in classrooms. https://files.eric.ed.gov/fulltext/ED567811.pdf
OpenAI. (2024). ChatGPT (GPT-4 model) [Large language
model]. https://chat.openai.com
Planar D and Moya S “The effectiveness of instructor personalized
and formative feedback provided by instructor in an online setting: some
unresolved issues” The Electronic Journal of e-Learning Volume 14 Issue 3 2016,
(pp196-203). www.ejel.org
Pozas, M., Letzel, V. and Schneider, C. (2020). "Teachers and differentiated
instruction: exploring differentiation practices to address student diversity." Journal
of Research in Special Educational Needs, 20: 217-230.
Stanford Center for Teaching and Learning. (2025). Differentiated
instruction. https://ctl.stanford.edu/differentiated-instruction
Turner, W.D., Solis, O.J., and Kincade, D.H. (2017). Differentiating instruction for large
classes in higher education, International Journal of Teaching and
Learning in Higher Education, 29(3), 490-500.
Vygotsky, L. S. (1978). Mind in society: The development of higher
psychological processes. Harvard University Press
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