Formative
assessments provide opportunities for ongoing feedback that is timely,
specific, and actionable. This feedback helps students understand their
progress and areas for improvement, while also guiding teachers in refining
their instructional practices to better support student learning. By
continually assessing student understanding and progress through formative
assessments, teachers gain insights into areas where students may be struggling
or excelling. This enables them to adjust their lesson plans and instructional
strategies to address specific learning needs, ensuring that teaching is
targeted and effective.
Formative assessment
approaches enable teachers to systematically collect and analyze data on
student learning. Formative assessments provide teachers with valuable data
about individual student strengths, weaknesses, and learning styles. With this
information, teachers can tailor instruction to meet the unique needs of each
student, offering personalized learning experiences that maximize engagement
and learning outcomes.
Proficiency
with formative
assessment strategies can be instrumental in helping teachers plan for the
authentic utilization of generative
artificial intelligence (AI). Formative
assessments provide insights into individual student needs, strengths, and
weaknesses. Understanding where students stand in their learning journey allows
teachers to tailor the implementation
of AI tools to meet specific needs.
Utilization
of formative assessments allows teachers to identify areas where students
require additional support or challenge. Effective deployment of AI tools can provide
personalized learning experiences, offering targeted interventions that address
each student's unique requirements. Formative assessments generate ongoing
feedback about student progress. Incorporating AI into the feedback loop can
allow teachers to enhance the quality
and timeliness of feedback provided to students.
AI-powered
systems can analyze student work in real-time, offering immediate feedback
and suggestions for improvement. By analyzing formative assessment data in
concert with AI insights, teachers can make informed decisions about resource
allocation. They can identify areas where additional AI tools or support may be
beneficial, thereby ensuring that resources effectively support student
learning.
Machine
learning, particularly through natural
language processing (NLP), can support teachers in planning for deep
learning by providing insights, resources, and personalized feedback. NLP
algorithms can analyze large amounts of text-based content, such as textbooks,
articles, or student essays, to extract key concepts, identify common themes,
and assess the complexity of the material. This helps teachers understand the
depth and breadth of topics covered in their curriculum, allowing them to plan
learning experiences that foster deep understanding.
Machine learning algorithms
can power adaptive learning platforms that personalize instruction based on
individual student needs and learning styles. By analyzing students' responses
to NLP-generated questions or prompts, these platforms can dynamically adjust
the difficulty and pace of instruction. This can result in targeted support,
helping students delve deeper into subject matter.
Natural
language processing (NLP)
algorithms can be used to analyze and provide feedback on student writing
assignments, discussions, or responses to open-ended questions. By
automatically evaluating the depth of student understanding, coherence of
arguments, and use of evidence, NLP-powered feedback tools can help teachers
identify areas where students need further exploration or clarification. By curating high-quality materials aligned
with specific learning goals, teachers can enrich their instructional planning
and provide students with diverse perspectives and resources for further
critical thinking. By monitoring student engagement in real-time, teachers can monitor
and adjust their instructional strategies to maintain student interest.
Machine
learning through natural language processing supports teachers in planning by
automating content analysis, personalizing instruction, providing text-based
feedback, facilitating resource curation, visualizing knowledge representation,
monitoring student engagement, and generating instructional materials. These
capabilities empower teachers to design learning experiences that foster understanding,
critical thinking, and meaningful interdisciplinary connections. Therefore, NLP-powered
natural language generation tools can assist teachers in creating detailed
lesson plans, instructional materials, and explanations tailored
to the needs of their students. Thus, these AI tools can free up teachers'
time for deeper engagement with students and more strategic planning of
learning experiences.
By
informing instructional decision-making,
personalizing learning experiences, and supporting ongoing professional
development, formative assessment approaches provide a foundation for teachers
to effectively plan for the authentic utilization of generative AI in the
classroom. However, while using
examples, materials, and contexts that resonate with students' cultural
backgrounds, experiences, and Funds
of Knowledge (Moll, González, & Amanti, 2009; Roe, 2019) ensures instruction is more culturally relevant and
inclusive of diverse perspectives, it does not ensure AI-powered systems will
be respective of Universal Design for Learning Principles and Guidelines
(Rose & Meyer, 2002; CAST, 2018).
This
is why working together as a Professional
Learning Community (PLC) continues to benefit educators seeking to identify
a clear, shared vision, developing a collaborative culture focusing on
learning, engaging in collective inquiry, remaining action oriented, committing
to continuous improvement, and being results oriented (Dufour et al., 2008). Working together as a PLC will help more
teachers learn to combine formative assessment data with AI insights to inform
professional development initiatives for all teachers. By identifying areas
where teachers may need additional support or training in integrating AI into
their instructional practices, professional development programs can be
tailored to meet these specific needs.
A
Professional Learning Community invites learning by doing.
This process is optimized through intentional, caring, optimistic, respectful,
and trustworthy (ICORT)
mindsets (Purkey
& Novak, 2016; Anderson, 2021) to foster a positive and supportive
learning environment where diverse learners feel valued, respected, and
motivated to engage in assessments. When students feel cared for and respected,
they are more likely to approach learning with confidence and enthusiasm
(Maslow, 1949). Formative assessment approaches provide a foundation for
teachers to effectively plan for the authentic utilization of generative AI in
the classroom by informing instructional decision-making, personalizing
learning experiences, and supporting ongoing professional development.
Do you want to learn more about authentic utilization of generative artificial intelligence (AI)? Good news: Google, Harvard, and more are offering FREE AI courses. You are invited to follow the links of your choice to access FREE courses designed to help you become more proficient with AI:
1.
Google AI Courses: Google
offers 5 different courses to learn generative AI from the ground up. You can begin
with an Introduction to AI and finish having a solid understanding of AI as a
whole. https://lnkd.in/eW5k4DVz
2. Introduction to AI with Python:
Yes, Harvard University is offering a full 7-week course to explore the
concepts and algorithms of AI. The course begins with the technologies behind
AI and ends with increased knowledge of AI principles and machine learning
libraries. https://lnkd.in/g4Sbb3nQ
3. Prompt Engineering for ChatGPT:
This is a 6-module course developed by Vanderbilt University. The modules start with providing beginners
with how to write better prompts. Effective prompting leads to knowing how to utilize
ChatGPT for reliable outputs. https://lnkd.in/d-rCb-AM
4.
ChatGPT Prompt Engineering for Devs: In collaboration with
DeepLearning, OpenAI is offering Isa Fulford and Andrew Ng’s course. Learners
can begin with best practices and finish with hands-on practice to exhibit
their better understanding of prompting. https://lnkd.in/gtGc5Znp
5.
Microsoft AI Course: Microsoft offers an AI course. Begin with an
introduction and, if you wish, continue through learning about neural networks
and deep learning. https://lnkd.in/eKJ9qmEQ
To Cite:
Anderson, C.J. (April 30, 2024) Formative
assessment approaches help teachers plan for
authentic utilization of generative Artificial
intelligence. [Web log post] Retrieved from
http://www.ucan-cja.blogspot.com/
References:
Anderson, C.
J. (2021). Developing your students' emotional intelligence and
philosophical perspective
begins with I-CORT. Journal of Invitational Theory and Practice,
27, 36-50.
Atlas, S. (2023) ‘ChatGPT for higher education and professional development: A guide to
conversational AI’,
College of Business Faculty Publications [Preprint]. Available at:
https://digitalcommons.uri.edu/cba_facpubs/548
DuFour, R., DuFour, R., & Eaker, R. (2008).
Revisiting professional learning communities at
work:
New insights for improving
schools.
Bloomington, IN: Solution Tree Press.
Dufour, R. (2006). Learning by doing: A handbook for professional learning
communities at
work. Bloomington, IN:
Solution Tree Press.
Roe. K. (2019) Supporting student assets and demonstrating respect for funds of knowledge. Journal of Invitational Theory and Practice, v25 p5-13
Rose, D. H., & Meyer, A. (2002). Teaching every student in the digital age: Universal design for learning. ASCD.
U.S. Department of Education, Office of Educational Technology (2023), Artificial Intelligence and Future of Teaching and Learning: Insights and Recommendations. Retrieved from: Artificial Intelligence and the Future of Teaching and Learning (ed.gov)
Wormeli, R. (2017). Fair isn't always equal: Assessment and grading in the differentiated
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Retrieved from https://eric.ed.gov/?id=ED592455
