Classroom AI Policies - Revision history

Jtodd1 at 19:54, 30 January 2026

2026-01-30T19:54:41Z

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	''''A workshop titled "Mini-Workshop: Classroom AI Policies" originally presented for [http://cat.xula.edu/ CAT+FD] on 27 January 2026 by Dr. Jason Todd''		''A workshop titled "Mini-Workshop: Classroom AI Policies" originally presented for [http://cat.xula.edu/ CAT+FD] on 27 January 2026 by Dr. Jason Todd''

	== Abstract ==		== Abstract ==

Jtodd1: Jtodd1 moved page AI Classroom Policies to Classroom AI Policies: Corrected presentation name

2026-01-30T19:53:58Z

Jtodd1 moved page AI Classroom Policies to Classroom AI Policies: Corrected presentation name

← Older revision	Revision as of 12:53, 30 January 2026
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Jtodd1 at 19:53, 30 January 2026

2026-01-30T19:53:12Z

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	{{#widget:YouTube\|id=SPMFrI-wqzM}}		{{#widget:YouTube\|id=SPMFrI-wqzM}}
	== Resources ==		== Resources ==
	*[[File:Classroom-AI-Policies.pdf\| Classroom AI Policies Slidedeck ~~(PDF)~~]]		*[[File:Classroom-AI-Policies.pdf\|Classroom AI Policies Slidedeck]]

	== References ==		== References ==

Jtodd1 at 19:51, 30 January 2026

2026-01-30T19:51:58Z

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	{{#widget:YouTube\|id=SPMFrI-wqzM}}		{{#widget:YouTube\|id=SPMFrI-wqzM}}
	== Resources ==		== Resources ==
	*[[File:Classroom-AI-Policies.pdf Classroom AI Policies Slidedeck (PDF)]]		*[[File:Classroom-AI-Policies.pdf\| Classroom AI Policies Slidedeck (PDF)]]

	== References ==		== References ==

Jtodd1 at 19:50, 30 January 2026

2026-01-30T19:50:37Z

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	Developing effective classroom AI policies requires moving beyond a simple binary of permission or restriction to a nuanced framework that considers specific course goals, institutional context, and the enhancement of student learning. Because a student’s personal ethical framework often influences their behavior more than mere awareness of rules, policies should prioritize educational transparency by clearly explaining the "why" behind boundaries and connecting them to disciplinary norms and learning outcomes. Instructors are encouraged to tailor policies for individual classes rather than applying a universal standard, acknowledging that AI can range from an academic integrity violation to a powerful research and editing tool depending on the assignment's purpose. As technology and institutional support systems evolve, maintaining an ongoing dialogue with students about ethical use and professional relevance ensures that these policies remain flexible and effective in fostering meaningful learning.		Developing effective classroom AI policies requires moving beyond a simple binary of permission or restriction to a nuanced framework that considers specific course goals, institutional context, and the enhancement of student learning. Because a student’s personal ethical framework often influences their behavior more than mere awareness of rules, policies should prioritize educational transparency by clearly explaining the "why" behind boundaries and connecting them to disciplinary norms and learning outcomes. Instructors are encouraged to tailor policies for individual classes rather than applying a universal standard, acknowledging that AI can range from an academic integrity violation to a powerful research and editing tool depending on the assignment's purpose. As technology and institutional support systems evolve, maintaining an ongoing dialogue with students about ethical use and professional relevance ensures that these policies remain flexible and effective in fostering meaningful learning.

	{{#widget:YouTube id=SPMFrI-wqzM}}		{{#widget:YouTube\|id=SPMFrI-wqzM}}
	== Resources ==		== Resources ==
	*[File:Classroom-AI-Policies.pdf Classroom AI Policies Slidedeck (PDF)]		*[[File:Classroom-AI-Policies.pdf Classroom AI Policies Slidedeck (PDF)]]

	== References ==		== References ==

Jtodd1 at 19:49, 30 January 2026

2026-01-30T19:49:25Z

Jtodd1 at 19:45, 30 January 2026

2026-01-30T19:45:55Z

← Older revision		Revision as of 12:45, 30 January 2026
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	== Resources ==		== Resources ==
	*[[Classroom-AI-Policies.pdf\|Classroom AI Policies Slidedeck (PDF)]]		*[[File:Classroom-AI-Policies.pdf\|Classroom AI Policies Slidedeck (PDF)]]
	*[[Resources for the TILT Framework.pdf\|Resources for the TILT Framework]]
	*[[Resources for Entry and Exit Tickets.pdf\| Resources for Entry and Exit Tickets]]

	== References ==		== References ==

Jtodd1 at 19:44, 30 January 2026

2026-01-30T19:44:27Z

← Older revision		Revision as of 12:44, 30 January 2026
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	''''A workshop titled "Mini-Workshop: Classroom AI Policies" originally presented for [http://cat.xula.edu/ CAT+FD] on 27 January 2026 by Dr. Jason Todd''		''''A workshop titled "Mini-Workshop: Classroom AI Policies" originally presented for [http://cat.xula.edu/ CAT+FD] on 27 January 2026 by Dr. Jason Todd''

			== Abstract ==
			Developing effective classroom AI policies requires moving beyond a simple binary of permission or restriction to a nuanced framework that considers specific course goals, institutional context, and the enhancement of student learning. Because a student’s personal ethical framework often influences their behavior more than mere awareness of rules, policies should prioritize educational transparency by clearly explaining the "why" behind boundaries and connecting them to disciplinary norms and learning outcomes. Instructors are encouraged to tailor policies for individual classes rather than applying a universal standard, acknowledging that AI can range from an academic integrity violation to a powerful research and editing tool depending on the assignment's purpose. As technology and institutional support systems evolve, maintaining an ongoing dialogue with students about ethical use and professional relevance ensures that these policies remain flexible and effective in fostering meaningful learning.

	{{#widget:YouTube\|id=SPMFrI-wqzM}}		{{#widget:YouTube\|id=SPMFrI-wqzM}}
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	[[Category: CAT+FD]]		[[Category: CAT+FD]]
	[[Category: CAT+FD 2025-2026]]		[[Category: CAT+FD 2025-2026]]
	''		[[Category: Artificial Intelligence]]
			[[Category: Classroom Policies]]

Jtodd1: Created page with "''''A workshop titled "Mini-Workshop: Classroom AI Policies" originally presented for [http://cat.xula.edu/ CAT+FD] on 27 January 2026 by Dr. Jason Todd'' {{#widget:YouTube|id=SPMFrI-wqzM}} == Resources == Classroom AI Policies Slidedeck (PDF) Resources for the TILT Framework * Resources for Entry and Exit Tickets == References == * Alsharefeen, R., &..."

2026-01-30T19:38:53Z

Created page with "''''A workshop titled "Mini-Workshop: Classroom AI Policies" originally presented for [http://cat.xula.edu/ CAT+FD] on 27 January 2026 by Dr. Jason Todd'' {{#widget:YouTube|id=SPMFrI-wqzM}} == Resources == *Classroom AI Policies Slidedeck (PDF) *Resources for the TILT Framework * Resources for Entry and Exit Tickets == References == * Alsharefeen, R., &..."

New page

''''A workshop titled "Mini-Workshop: Classroom AI Policies" originally presented for [http://cat.xula.edu/ CAT+FD] on 27 January 2026 by Dr. Jason Todd''

{{#widget:YouTube|id=SPMFrI-wqzM}}

== Resources ==
*[[Classroom-AI-Policies.pdf|Classroom AI Policies Slidedeck (PDF)]]
*[[Resources for the TILT Framework.pdf|Resources for the TILT Framework]]
*[[Resources for Entry and Exit Tickets.pdf| Resources for Entry and Exit Tickets]]

== References ==
* Alsharefeen, R., & Sayari, N. A. (2025). Examining academic integrity policy and practice in the era of AI: a case study of faculty perspectives. Frontiers in Education. [[https://doi.org/10.3389/feduc.2025.1621743| https://doi.org/10.3389/feduc.2025.1621743]]
* Deep, P. D., Edgington, W. D., Ghosh, N., & Rahaman, Md. S. (2025). Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education. Information, 16(10), 905. [[https://doi.org/10.3390/info16100905| https://doi.org/10.3390/info16100905]]
* Gustilo, L., Ong, E., & Lapinid, M. R. (2024). Algorithmically-driven writing and academic integrity: exploring educators’ practices, perceptions, and policies in AI era. International Journal for Educational Integrity. [[https://doi.org/10.1007/s40979-024-00153-8| https://doi.org/10.1007/s40979-024-00153-8]]
* Hamerman, E. J., Aggarwal, A., & Martins, C. M. (2024). An investigation of generative AI in the classroom and its implications for university policy. Quality Assurance in Education. [[https://doi.org/10.1108/qae-08-2024-0149| https://doi.org/10.1108/qae-08-2024-0149]]
* Kangwa, D., Msafiri, M. M., & Fute, A. (2025). Exploring the Factors That Promote a Balance Between Academic Integrity and the Effective Use of GenAI Tools in Higher Education: A Systematic Review. Journal of Computer Assisted Learning, 41(5). [[https://doi.org/10.1111/jcal.70109| https://doi.org/10.1111/jcal.70109]]
* Lund, B., Mannuru, N. R., Teel, Z. A., Lee, T. H., Ortega, N. J., Simmons, S., & Ward, E. (2025). Student Perceptions of AI-Assisted Writing and Academic Integrity: Ethical Concerns, Academic Misconduct, and Use of Generative AI in Higher Education. AI in Education, 1(1), 2. [[https://doi.org/10.3390/aieduc1010002| https://doi.org/10.3390/aieduc1010002]]
* Plata, S., De Guzman, M. A., & Quesada, A. (2023). Emerging Research and Policy Themes on Academic Integrity in the Age of Chat GPT and Generative AI. Asian Journal of University Education, 19(4), 743–758. [[https://doi.org/10.24191/ajue.v19i4.24697| https://doi.org/10.24191/ajue.v19i4.24697]]
* Streletska, N., Ulishchenko, A., Klieba, A., Vlasiuk, I., & Genkal, S. (2024). Integrating artificial intelligence into STEM education: Navigating academic integrity. Multidisciplinary Reviews, 8, 2024spe069. [[https://doi.org/10.31893/multirev.2024spe069| https://doi.org/10.31893/multirev.2024spe069]]
* Sumilong, M. J. (2025). Instructional affect and learner motivation in generative AI-restrictive and permissive classrooms. Frontiers in Education, 10. [[https://doi.org/10.3389/feduc.2025.1626802| https://doi.org/10.3389/feduc.2025.1626802]]
* Xie, Y., Chen, X., Ren, Z., & Su, W. J. (2025). Watermark in the Classroom: A Conformal Framework for Adaptive AI Usage Detection.

[[Category: CAT+FD]]
[[Category: CAT+FD 2025-2026]]
''

← Older revision		Revision as of 12:49, 30 January 2026
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	Developing effective classroom AI policies requires moving beyond a simple binary of permission or restriction to a nuanced framework that considers specific course goals, institutional context, and the enhancement of student learning. Because a student’s personal ethical framework often influences their behavior more than mere awareness of rules, policies should prioritize educational transparency by clearly explaining the "why" behind boundaries and connecting them to disciplinary norms and learning outcomes. Instructors are encouraged to tailor policies for individual classes rather than applying a universal standard, acknowledging that AI can range from an academic integrity violation to a powerful research and editing tool depending on the assignment's purpose. As technology and institutional support systems evolve, maintaining an ongoing dialogue with students about ethical use and professional relevance ensures that these policies remain flexible and effective in fostering meaningful learning.		Developing effective classroom AI policies requires moving beyond a simple binary of permission or restriction to a nuanced framework that considers specific course goals, institutional context, and the enhancement of student learning. Because a student’s personal ethical framework often influences their behavior more than mere awareness of rules, policies should prioritize educational transparency by clearly explaining the "why" behind boundaries and connecting them to disciplinary norms and learning outcomes. Instructors are encouraged to tailor policies for individual classes rather than applying a universal standard, acknowledging that AI can range from an academic integrity violation to a powerful research and editing tool depending on the assignment's purpose. As technology and institutional support systems evolve, maintaining an ongoing dialogue with students about ethical use and professional relevance ensures that these policies remain flexible and effective in fostering meaningful learning.

	{{#widget:YouTube\|id=SPMFrI-wqzM}}		{{#widget:YouTube id=SPMFrI-wqzM}}

	== Resources ==		== Resources ==
	*[[File:Classroom-AI-Policies.pdf\|Classroom AI Policies Slidedeck (PDF)]]		*[File:Classroom-AI-Policies.pdf Classroom AI Policies Slidedeck (PDF)]

	== References ==		== References ==
	* Alsharefeen, R., & Sayari, N. A. (2025). Examining academic integrity policy and practice in the era of AI: a case study of faculty perspectives. Frontiers in Education. [[https://doi.org/10.3389/feduc.2025.1621743\| https://doi.org/10.3389/feduc.2025.1621743]]		* Alsharefeen, R., & Sayari, N. A. (2025). Examining academic integrity policy and practice in the era of AI: a case study of faculty perspectives. Frontiers in Education. [https://doi.org/10.3389/feduc.2025.1621743 https://doi.org/10.3389/feduc.2025.1621743]
	* Deep, P. D., Edgington, W. D., Ghosh, N., & Rahaman, Md. S. (2025). Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education. Information, 16(10), 905. [[https://doi.org/10.3390/info16100905\| https://doi.org/10.3390/info16100905]]		* Deep, P. D., Edgington, W. D., Ghosh, N., & Rahaman, Md. S. (2025). Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education. Information, 16(10), 905. [https://doi.org/10.3390/info16100905 https://doi.org/10.3390/info16100905]
	* Gustilo, L., Ong, E., & Lapinid, M. R. (2024). Algorithmically-driven writing and academic integrity: exploring educators’ practices, perceptions, and policies in AI era. International Journal for Educational Integrity. [[https://doi.org/10.1007/s40979-024-00153-8\| https://doi.org/10.1007/s40979-024-00153-8]]		* Gustilo, L., Ong, E., & Lapinid, M. R. (2024). Algorithmically-driven writing and academic integrity: exploring educators’ practices, perceptions, and policies in AI era. International Journal for Educational Integrity. [https://doi.org/10.1007/s40979-024-00153-8 https://doi.org/10.1007/s40979-024-00153-8]
	* Hamerman, E. J., Aggarwal, A., & Martins, C. M. (2024). An investigation of generative AI in the classroom and its implications for university policy. Quality Assurance in Education. [[https://doi.org/10.1108/qae-08-2024-0149\| https://doi.org/10.1108/qae-08-2024-0149]]		* Hamerman, E. J., Aggarwal, A., & Martins, C. M. (2024). An investigation of generative AI in the classroom and its implications for university policy. Quality Assurance in Education. [https://doi.org/10.1108/qae-08-2024-0149 https://doi.org/10.1108/qae-08-2024-0149]
	* Kangwa, D., Msafiri, M. M., & Fute, A. (2025). Exploring the Factors That Promote a Balance Between Academic Integrity and the Effective Use of GenAI Tools in Higher Education: A Systematic Review. Journal of Computer Assisted Learning, 41(5). [[https://doi.org/10.1111/jcal.70109\| https://doi.org/10.1111/jcal.70109]]		* Kangwa, D., Msafiri, M. M., & Fute, A. (2025). Exploring the Factors That Promote a Balance Between Academic Integrity and the Effective Use of GenAI Tools in Higher Education: A Systematic Review. Journal of Computer Assisted Learning, 41(5). [https://doi.org/10.1111/jcal.70109 https://doi.org/10.1111/jcal.70109]
	* Lund, B., Mannuru, N. R., Teel, Z. A., Lee, T. H., Ortega, N. J., Simmons, S., & Ward, E. (2025). Student Perceptions of AI-Assisted Writing and Academic Integrity: Ethical Concerns, Academic Misconduct, and Use of Generative AI in Higher Education. AI in Education, 1(1), 2. [[https://doi.org/10.3390/aieduc1010002\| https://doi.org/10.3390/aieduc1010002]]		* Lund, B., Mannuru, N. R., Teel, Z. A., Lee, T. H., Ortega, N. J., Simmons, S., & Ward, E. (2025). Student Perceptions of AI-Assisted Writing and Academic Integrity: Ethical Concerns, Academic Misconduct, and Use of Generative AI in Higher Education. AI in Education, 1(1), 2. [https://doi.org/10.3390/aieduc1010002 https://doi.org/10.3390/aieduc1010002]
	* Plata, S., De Guzman, M. A., & Quesada, A. (2023). Emerging Research and Policy Themes on Academic Integrity in the Age of Chat GPT and Generative AI. Asian Journal of University Education, 19(4), 743–758. [[https://doi.org/10.24191/ajue.v19i4.24697\| https://doi.org/10.24191/ajue.v19i4.24697]]		* Plata, S., De Guzman, M. A., & Quesada, A. (2023). Emerging Research and Policy Themes on Academic Integrity in the Age of Chat GPT and Generative AI. Asian Journal of University Education, 19(4), 743–758. [https://doi.org/10.24191/ajue.v19i4.24697 https://doi.org/10.24191/ajue.v19i4.24697]
	* Streletska, N., Ulishchenko, A., Klieba, A., Vlasiuk, I., & Genkal, S. (2024). Integrating artificial intelligence into STEM education: Navigating academic integrity. Multidisciplinary Reviews, 8, 2024spe069. [[https://doi.org/10.31893/multirev.2024spe069\| https://doi.org/10.31893/multirev.2024spe069]]		* Streletska, N., Ulishchenko, A., Klieba, A., Vlasiuk, I., & Genkal, S. (2024). Integrating artificial intelligence into STEM education: Navigating academic integrity. Multidisciplinary Reviews, 8, 2024spe069. [https://doi.org/10.31893/multirev.2024spe069 https://doi.org/10.31893/multirev.2024spe069]
	* Sumilong, M. J. (2025). Instructional affect and learner motivation in generative AI-restrictive and permissive classrooms. Frontiers in Education, 10. [[https://doi.org/10.3389/feduc.2025.1626802\| https://doi.org/10.3389/feduc.2025.1626802]]		* Sumilong, M. J. (2025). Instructional affect and learner motivation in generative AI-restrictive and permissive classrooms. Frontiers in Education, 10. [https://doi.org/10.3389/feduc.2025.1626802 https://doi.org/10.3389/feduc.2025.1626802]
	* Xie, Y., Chen, X., Ren, Z., & Su, W. J. (2025). Watermark in the Classroom: A Conformal Framework for Adaptive AI Usage Detection.		* Xie, Y., Chen, X., Ren, Z., & Su, W. J. (2025). Watermark in the Classroom: A Conformal Framework for Adaptive AI Usage Detection.