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Abstract

Artificial intelligence (AI) has and will continue to transform education for years to come. While researchers tend to focus on the impact of AI on teaching and learning, relatively few have considered the impact of AI on school leadership, particularly instructional leadership, and specifically teacher supervision—an aspect of school leadership that possesses the closest connection to AI’s origins in education. In this paper, we first describe the role of school leaders as instructional leaders. We then move to consider one aspect of instructional leadership—teacher supervision—and its theoretical and empirical understandings. In the second section of the paper, we outline various promises and pitfalls of using AI for teacher supervision using existing research on AI in education that informs key tasks and skills of teacher supervisors—observation and feedback. In our final section, we combine our understandings of teacher supervision and our considerations of using AI for teacher supervision to outline promising avenues for research.

Keywords

artificial intelligence teacher supervision instructional leadership classroom observation feedback

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References

Anthony

Hall

Krell

M. W.

Eccles

C. M.

(2023). Providing feedback for evaluation and professional learning. In Lavigne

A. L.

Derrington

M. L.

(Eds.), Actionable feedback to Pk-12 teachers (pp. 23–34). Rowman & Littlefield. https://doi.org/10.5040/9798881820701.ch-002

Ashwin

T. S.

Guddeti

R. M. R.

(2020). Automatic detection of students’ affective states in classroom environment using hybrid convolutional neural networks. Education and Information Technologies, 25, 1387–1415. https://doi.org/10.1007/s10639-019-10004-6

Ayyildiz

Yilmaz

(2023). A new chapter is being written about writing instruction: Instructional leadership at K-12 levels in the age of artificial intelligence (AI). Educational Policy Analysis and Strategic Research, 18(4), 82–101. https://doi.org/10.29329/epasr.2023.631.4

Balyer

Özcan

(2020). School principals’ instructional feedback to teachers: Teachers’ views. International Journal of Curriculum and Instruction, 12, 295–312.

Bellibaş

M. S.

Gümüs

Liu

(2020). Does school leadership matter for teachers’ classroom practice? The influence of instructional leadership and distributed leadership on instructional quality. School Effectiveness and School Improvement, 32(3), 387–412. https://doi.org/10.1080/09243453.2020.1858119

Benigno

S. D.

(2017). Improving the supervision process: Trust, presence, empowerment and recognition. Advances in Social Sciences Research Journal, 4(3), 1475. https://doi.org/10.14738/assrj.43.2689

Berkovich

(2023). The great resignation: Exploring the effect of regular and digital instructional leadership on teachers’ intention to leave. Management in Education, 11, 1–9. https://doi.org/10.1177/08920206231163984

Blase

(1999). Effective instructional leadership: Teachers perspectives on how principals promote teaching and learning in schools. Journal of Educational Administration, 38(2), 130–141. https://doi.org/10.1108/09578230010320082

Butler

Boggs

(2023). Commanding through change: A conceptual model for school leadership amid gentrification. Journal of School Leadership, 33(5), 452–471. https://doi.org/10.1177/10526846231169780

10.

Chen

X. M.

Liao

X. L.

Chen

I. H.

Gamble

J. H.

Jiang

X. Y.

X. D.

C. X.

(2024). The long-term effects of perceived instructional leadership on teachers’ psychological well-being during COVID-19. PLoS One, 19(8), e0305494. https://doi.org/10.1371/journal.pone.0305494

11.

Cherasaro

T. L.

Brodersen

R. M.

Reale

M. L.

Yanoski

D. C.

(2016). Teachers’ responses to feedback from evaluators: What feedback characteristics matter? (REL 2017-190). In Regional educational laboratory central. U.S. Department of Education, Institute of Education, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Central.

12.

Chu

H.-C.

Hwang

G.-H.

Y.-F.

Yang

K.-H.

(2022). Roles and research trends in artificial intelligence in higher education: A systematic review of the top 50 most-cited articles. Australasian Journal of Educational Technology, 38(3), 22–42. https://doi.org/10.14742/ajet.7526

13.

Cormier

R. D.

Mette

I. M.

Oliveras

(2024). Culturally responsive instructional supervision: Leadership for equitable and emancipatory outcomes. Teachers College Press.

14.

Cotman

Guerra

Baker

A. M.

(2023). Culturally responsive instructional supervision: Further analysis of a leading textbook. Journal of Educational Supervision, 6(1), 45–63. https://doi.org/10.31045/jes.6.1.4

15.

Dai

C.-P.

(2023). Enhancing learning achievements and self-efficacy for preservice teachers using model-based support in simulation-based learning with artificial intelligence-powered virtual agents: Florida State University.

16.

Dai

McReynolds

Whitehill

(2023). In the search of negative moments: Multi-modal analysis of teacher negativity in classroom observation videos. In: Proceedings of the 16th international conference on educational data mining (pp. 278–285). International Educational Data Mining Society. Bengaluru, India, July 2023.

17.

Day

Sammons

(2016). The impact of leadership on student outcomes: How successful school leaders use transformational and instructional strategies to make a difference. Educational Administration Quarterly, 52(2), 221–258. https://doi.org/10.1177/0013161X15616863

18.

Defi

W. F.

Utama

Y. N.

(2024). Integrating instructional leadership and teaching tools of AI in a class: An analysis for the ethical dimensions. In: Proceedings of the International Conference on Religion. Science and Education, Vol. 3, pp. 277–283.

19.

Demszky

Liu

Hill

H. C.

Sanghi

Chung

(2023). Improving teachers’ questioning quality through automated feedback: A mixed-methods randomized controlled trial in brick-and-mortar classrooms. EdWorkingPaper, 23–875. Retrieved from Annenberg Institute at Brown University: https://doi.org/10.26300/8pnw-5q67

20.

Dexter

Clement

Moraguez

&Watson

G. S.

(2020). (Inter) active learning tools and pedagogical strategies in educational leadership preparation. Journal of Research on Leadership Education, 15(3), 173–191. https://doi.org/10.1177/1942775120936299

21.

Dogan

Arslan

(2025). The role of artificial intelligence in school leadership. Revista de Pedagogie Digitala, 4(1), 23–30. Bucureşti: Institutul pentru Educație. https://doi.org/10.61071/RPD.2531

22.

Dogan

M. E.

Goru Dogan

Bozkurt

(2023). The use of artificial intelligence (AI) in online learning and distance education processes: A systematic review of empirical studies. Applied Science, 13(5), 3056. https://doi.org/10.3390/app13053056

23.

Donaldson

M. L.

Cobb

C. D.

LeChasseur

Gabriel

Gonzales

Woulfin

Makuch

(2014). An evaluation of the pilot implementation of Connecticut’s system for educator effectiveness and development . Retrieved from. https://www.connecticutseed.org/wp-content/…/Neag_Final_SEED_Report_1-1-2014.pdf

24.

Donaldson

M. L.

Woulfin

(2018). From tinkering to going “rouge”: How principals use agency when enacting new teacher evaluation systems. Educational Evaluation and Policy Analysis, 40(4), 531–556. https://doi.org/10.3102/0162373718784205

25.

Dynarski

(2016). Teacher observations have been a waste of time and money. The Brookings Institution. Retrieved from. https://www.brookings.edu/research/teacher-observations-have-been-a-waste-of-time-and-money/

26.

Ergün

(2020). The effect of school transparency on attitude towards supervision. International Journal of Contemporary Educational Research, 7(1), 114–126. https://files.eric.ed.gov/fulltext/EJ1262375.pdf

27.

Ezeaku

S. N.

(2025). Instructional leadership in the era of AI: Correlates to open school climate in secondary schools in Anambra state. Unizik Journal of Educational Research and Policy Studies, 19(1), 148–160.

28.

Flores

M. A.

Derrington

M. L.

(2017). School principals’ views of teacher evaluation policy: Lessons learned from two empirical studies. International Journal of Leadership in Education, 20(4), 416–431. https://doi.org/10.1080/13603124.2015.1094144

29.

Ford

Baer

Yapanel

Gray

(2008). The LENA language environment analysis system (LENA foundation technical report LTR-03-02): LENA Foundation.

30.

Gay

(2018). Culturally responsive teaching: Theory, research, and practice (3rd ed.). Teachers College Press.

31.

Ghamrawi

Shal

Ghamrawi

N. A. R.

(2024). Exploring the impact of AI on teacher leadership: Regressing or expanding? Education and Information Technologies, 29(7), 8415–8433. https://doi.org/10.1007/s10639-023-12174-w

32.

Gilson

L. L.

Goldberg

C. B.

(2015). Editors’ comment: So, what is a conceptual paper? Group & Organization Management, 40(2), 127–130. https://doi.org/10.1177/1059601115576425

33.

Glanz

Hazi

H. M.

(2019). Shedding light on the phenomenon of supervision traveling incognito: A field’s struggles for visibility. Journal of Educational Supervision, 2(1), 1. https://doi.org/10.31045/jes.2.1.1

34.

Glickman

C. D.

(1985). Development as the aim of instructional supervision [paper presentation]: Annual Meeting of the Association for Supervision and Curriculum Development. https://files.eric.ed.gov/fulltext/ED263655.pdf

35.

Glickman

C. D.

Gordon

S. P.

(1987). Clarifying developmental supervision. Educational Leadership, 44(8), 64–68.

36.

Glickman

C. D.

Gordon

S. P.

Ross-Gordon

J. M.

(2018). Supervision and instructional leadership: A developmental approach (10th ed.). Pearson.

37.

Goddard

R. D.

Balles

L. P.

Kim

(2021). Principal efficacy beliefs for instructional leadership and their relation to teachers’ sense of collective efficacy and student achievement. Leadership and Policy in Schools, 20(3), 472–493. https://doi.org/10.1080/15700763.2019.1696369

38.

Goddard

Y. L.

Neumerski

C. M.

Goddard

R. D.

Salloum

Berebitsky

(2010). A multilevel exploratory study of the relationship between teachers’ perceptions of principals’ instructional support and group norms for instruction in elementary schools. The Elementary School Journal, 111(2), 336–357. https://doi.org/10.1086/656303

39.

Goldhammer

(1969). Clinical supervision: Special methods for the supervision of teachers. Holt, Rinehart and Winston.

40.

Goldring

Grissom

Neumerski

C. M.

Murphy

Blissett

(2015). Making time for instructional leadership. The evoluation of the SAM process (Volume I). https://wallacefoundation.org/sites/default/files/2023-10/Making-Time-for-Instructional-Leadership-Vol-1.pdf

41.

Gordon

S. P.

(1990). Developmental supervision: An exploratory study of a promising model. Journal of Curriculum and Supervision, 5(4), 293–307.

42.

Grissom

J. A.

Bartanen

(2022). Potential race and gender biases in high-stakes teacher observations. Journal of Policy Analysis and Management, 41(1), 131–161. https://doi.org/10.1002/pam.22352

43.

Guskey

T. R.

(2002). Professional development and teacher change. Teachers and Teaching, 8(3), 381–391. https://doi.org/10.1080/135406002100000512

44.

Gümüş

Hallinger

Cansoy

Bellibaş

M. S.

(2021). Instructional leadership in a centralized and competitive educational system: A qualitative meta-synthesis of research from Turkey. Journal of Educational Administration, 59(6), 702–720. https://doi.org/10.1108/JEA-04-2021-0073

45.

Hallinger

(2005). Instructional leadership and the school principal: A passing fancy that refuses to fade away. Leadership and Policy in Schools, 4(3), 221–239. https://doi.org/10.1080/15700760500244793

46.

Herlihy

Karger

Pollard

Hill

H. C.

Kraft

M. A.

Williams

Howard

(2014). State and local efforts to investigate the validity and reliability of scores from teacher evaluation systems. Teachers College Record, 116(1), 1–8. https://doi.org/10.1177/016146811411600108

47.

Herold

(2019). Forty percent of elementary school teachers’ work could be automated by 2030. McKinsey Global Institute Predicts. Education Week. https://blogs.edweek.org/edweek/DigitalEducation/2019/06/women_future_of_work_mckinsey.html

48.

Hinojo-Lucena

F.-J.

Aznar-Díaz

Cáceres-Reche

M.-P.

Romero-Rodríguez

J.-M.

(2019). Artificial intelligence in higher education: A bibliometric study on its impact in the scientific literature. Education Sciences, 9(1), 51. https://doi.org/10.3390/educsci9010051

49.

Hunter

S. B.

Springer

M. G.

(2022). Critical feedback characteristics, teacher human capital, and early-career teacher performance: A mixed-methods analysis. Educational Evaluation and Policy Analysis, 44(3), 380–403. https://doi.org/10.3102/01623737211062913

50.

Ingersoll

R. M.

May

(2016). Minority teacher recruitment, employment, and retention: 1987 to 2013. Learning Policy Institute. Research Brief. https://learningpolicyinstitute.org/sites/default/files/product-files/Minority_Teacher_Recruitment_Employment_Retention_BRIEF.pdf

51.

Jaakkola

(2020). Designing conceptual articles: Four approaches. AMS Review, 10(1-2), 18–26. https://doi.org/10.1007/s13162-020-00161-0

52.

Jiang

J. Y.

Sporte

S. E.

Luppescu

(2015). Teacher perspectives on evaluation reform: Chicago’s REACH. Educational Researcher, 44(2), 105–116. https://doi.org/10.3102/0013189x15575517

53.

Kamalov

Santandreu

C. D.

Gurrib

(2023). New era of artificial intelligence in education: Towards a sustainable multifaceted revolution. Sustainability, 15(16), Article 12451. https://doi.org/10.3390/su151612451

54.

Kamin

S. J.

Donaldson

M. L.

(2024). Complexities in context: Uncovering differences in school leaders’ needs and mentoring based on school environment. Journal of Research on Leadership Education, 20(2), 19427751241266876. https://doi.org/10.1177/19427751241266876

55.

Karakose

Tülübaş

(2023). How can ChatGPT facilitate teaching and learning: Implications for contemporary education. Educational Process: International Journal, 12(4), 7–16. https://doi.org/10.22521/edupij.2023.124.1

56.

Karakose

Tülübaş

(2024). School leadership and management in the age of artificial intelligence (AI): Recent developments and future prospects. Educational Process: International Journal, 13(1), 7–14. https://doi.org/10.22521/edupij.2024.131.1

57.

Khairullah

S. A.

Harris

Hadi

H. J.

Sandhu

R. A.

Ahmad

Alshara

M. A.

(2025). Implementing artificial intelligence in academic and administrative processes through responsible strategic leadership in higher education institutions. Frontiers in Education, 10, 1–24. https://doi.org/10.3389/feduc.2025.1548104

58.

Khalifa

(2020). Culturally responsive school leadership (4th ed.). Harvard Education Press.

59.

Kim

Wright

(2024). Navigating emotional discomfort in developing equity-driven school leaders: A conceptual-pedagogical framework. Journal of School Leadership, 34(5), 465–488. https://doi.org/10.1177/10526846241254050

60.

Kraft

M. A.

Christian

(2022). Can teacher evaluation systems produce high-quality feedback? An administrator training field experiment. American Educational Research Journal, 59(3), 500–537. https://doi.org/10.3102/00028312211024603

61.

Kraft

M. A.

Gilmour

A. F.

(2016). Can principals promote teacher development as evaluators? A case study of principals’ views and experiences. Educational Administration Quarterly, 52(5), 711–753. https://doi.org/10.1177/0013161X16653445

62.

Langreo

(2023). Can AI do teacher observation and deliver PD? In some schools, it already does. EducationWeek. https://www.edweek.org/leadership/can-ai-do-teacher

63.

Lasagabaster

Sierra

J. M.

(2011). Classroom observation: Desirable conditions established by teachers. European Journal of Teacher Education, 34(4), 449–454. https://doi.org/10.1080/02619768.2011.587113

64.

Lavigne

A. L.

Chamberlain

(2017). Teacher evaluation in Illinois: School leaders’ perceptions and practices. Educational Assessment, Evaluation and Accountability, 29(2), 179–209. https://doi.org/10.1007/s11092-016-9250-0

65.

Le Fevre

D. M.

Robinson

(2015). The interpersonal challenges of instructional leadership: Principals’ effectiveness in conversations about performance issues. Educational Administration Quarterly, 51(1), 58–95. https://doi.org/10.1177/0013161x13518218

66.

Leithwood

Sun

Schumacker

Hua

(2023). Psychometric properties of the successful school leadership survey. Journal of Educational Administration, 61(4), 386–404. https://doi.org/10.1108/jea-08-2022-0115

67.

Liu

Cohen

(2021). Measuring teaching practices at scale: A novel application of text-as-data methods. Educational evaluation and policy analysis. Advance online publication.

68.

Lochmiller

C. R.

(2016). Examining administrators’ instructional feedback to high school math and science teachers. Educational Administration Quarterly, 52(1), 75–109. https://doi.org/10.1177/0013161x15616660

69.

Lochmiller

C. R.

(2019). Credibility in instructional supervision: A catalyst for differentiated supervision. In: Differentiated teacher evaluation and professional learning: Policies and practices for promoting career growth. Springer. pp. 83–105.

70.

Chen

Shi

Zheng

(2023). Research on the application framework of intelligent technology to promote teachers’ classroom teaching evaluation. Frontiers of Education in China, 18(2), 171–186.

71.

Luo

Zhang

(2018). Auto-evaluation of classroom teaching & learning and its preliminary research findings. Modern Educational Technology, (8), 38–44.

72.

Luo

Zhao

Duan

(2021). Automatic analysis of classroom teaching process based on teachers’ near-field speech. Modern Educational Technology, 8, 76–84.

73.

Mangin

M. M.

Dunsmore

(2015). How the framing of instructional coaching as a lever for systemic or individual reform influences the enactment of coaching. Educational Administration Quarterly, 51(2), 179–213. https://doi.org/10.1177/0013161X14522814

74.

Marx

Lavigne

A. L.

Braden

Hawkman

Andersen

Gailey

Geddes

Jones

Washburn

(2023). “I didn’t quit. The system quit me.”: Why teachers of color leave teaching. International Journal of Leadership in Education, 21, 1–24. https://doi.org/10.1080/13603124.2023.2218113

75.

Mette

I. M.

(2019). The state of supervision discourse communities: A call for the future of supervision to shed its mask. Journal of Educational Supervision, 2(2), 1. https://doi.org/10.31045/jes.2.2.1

76.

Mette

I. M.

Cormier

D. R.

Oliveras-Ortiz

(2023b). Culturally responsive instructional supervision: (re)envisioning feedback for equitable change. In Lavigne

A. L.

Derrington

M. L.

(Eds.), Actionable feedback to Pre-K12 teachers (pp. 75–88). Rowman & Littlefield.

77.

Mette

I. M.

Cormier

R. D.

Oliveras

(2023a). Making a difference: Instructional leadership that drives self-reflection and values the expertise of teachers. Rowman & Littlefield.

78.

Murphy

(1990). Principal instructional leadership. In: Advances in educational administration, 1(Part B: Changing perspectives on the school). Journal of Educational Administration, 53(4), 492–511. https://doi.org/10.1108/JEA-01-2014-0006

79.

Nasreen

Shah

(2019). Effect of instructional supervisory practices on teacher motivation in private secondary schools of Lahore, Pakistan. Advances in Social Sciences Research Journal, 6(11), 192–203. https://doi.org/10.14738/assrj.611.7301

80.

National Center for Education Statistics . (2019). TALIS 2018 results. https://nces.ed.gov/pubs2019/2019132.pdf

81.

Nawab

Noor

(2023). “my supervisors first check school cleanliness”: Factors inhibiting instructional leadership practices in northern Sindh, Pakistan. Educational Management Administration & Leadership, 53(3), 1–16. https://doi.org/10.1177/17411432231177532

82.

Neumerski

C. M.

(2012). Rethinking instructional leadership, a review: What do we know about principal, teacher, and coach instructional leadership, and where should we go from here? Educational Administration Quarterly, 49(2), 310–347. https://doi.org/10.1177/0013161x12456700

83.

Parylo

Zepeda

(2014). Describing an effective principal: Perceptions of the central office leaders. School Leadership & Management, 34(5), 1–20. https://doi.org/10.1080/13632434.2014.928684

84.

Prates

M. O.

Avelar

P. H.

Lamb

L. C.

(2020). Assessing gender bias in machine translation: A case study with google translate. Neural Computing & Applications, 32(10), 6363–6381. https://doi.org/10.1007/s00521-019-04144-6

85.

Quebec Fuentes

Jimerson

J. B.

Bloom

M. A.

(2023). Building leadership content knowledge to supervise teachers in STEM disciplines. In Lavigne

A. L.

Derrington

M. L.

(Eds.), Actionable feedback to Pre-K12 teachers (pp. 107–122). Rowman & Littlefield.

86.

Range

B. G.

Young

Hvidston

(2013). Teacher perceptions about observation conferences: What do teachers think about their formative supervision in one US school district? School Leadership & Management, 33(1), 61–77. https://doi.org/10.1080/13632434.2012.724670

87.

Rigby

J. G.

Larbi-Sharif

Rosenquist

B. A.

Sharpe

C. J.

Cobb

Smith

(2017). Administrator observation and feedback: Does it lead to improvement in inquiry-oriented math instruction? Educational Administration Quarterly, 53(3), 475–516. https://doi.org/10.1177/0013161X16687006

88.

Robinson

V. M.

Lloyd

C. A.

Rowe

K. J.

(2008). The impact of leadership on student outcomes: An analysis of the differential effects of leadership types. Educational Administration Quarterly, 44(5), 635–674. https://doi.org/10.1177/0013161X08321509

89.

Rybinksi

Kopciuszewska

(2021). Will artificial intelligence revolutionise the student evaluation of teaching? A big data study of 1.6 million student reviews. Assessment & Evaluation in Higher Education, 46(7), 1127–1139. https://doi.org/10.1080/02602938.2020.1844866

90.

Sacharidis

(2024). Fairness and explainability for enabling trust in AI systems. In Ferwerda

Graus

Germanakos

Tkalčič

(Eds.), A human-centered perspective of intelligent personalized environments and systems. Human–Computer Interaction Series. Cham: Springer. https://doi.org/10.1007/978-3-031-55109-3_3

91.

Safsouf

Mansouri

Poirier

(2021). Experimental design of learning analysis dashboards for teachers and learners. In: Proceedings of the eighth ACM conference on learners: ACM 2021. pp. 347–350.

92.

Sedlacek

Sedova

(2017). How many are talking? The role of collectivity in dialogic teaching. International Journal of Educational Research, 85, 99–108. https://doi.org/10.1016/j.ijer.2017.07.001

93.

Sedova

Sedlacek

Svaricek

Majcik

Navratilova

Drexlerova

Kychler

Salamounova

(2019). Do those who talk more learn more? The relationship between student classroom talk and student achievement. Learning and Instruction, 63, 101–217. https://doi.org/10.1016/j.learninstruc.2019.101217

94.

Seo

Tang

Roll

Fels

Yoon

(2021). The impact of artificial intelligence on learner-instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18(54), 54. https://doi.org/10.1186/s41239-021-00292-9

95.

Shapira-Lischinsky

(2011). Teachers’ critical incidents: Ethical dilemmas in teaching practice. Teaching and Teacher Education, 27(3), 648–656. https://doi.org/10.1016/j.tate.2010.11.003

96.

Southworth

(2002). Instructional leadership in schools: Reflections and empirical evidence. School Leadership & Management, 22(1), 73–91. https://doi.org/10.1080/13632430220143042

97.

Stein

M. K.

Nelson

B. S.

(2003). Leadership content knowledge. Educational Evaluation and Policy Analysis, 25(4), 423–446. https://doi.org/10.3102/01623737025004423

98.

Supovitz

Sirinides

May

(2010). How principals and peers influence teaching and learning. Educational Administration Quarterly, 46(1), 31–56. https://doi.org/10.1177/1094670509353043

99.

Tesfaw

T. A.

Hofman

R. H.

(2014). Relationship between instructional supervision and professional development. International Education Journal: Comparative Perspectives, 13(1), 82–99.

100.

Torraco

R. J.

(2005). Writing integrative literature reviews: Guidelines and examples. Human Resource Development Review, 4(3), 356–367. https://doi.org/10.1177/1534484305278283

101.

Verner

Cuperman

Reitman

(2021). Exploring robot connectivity and collaborative sensing in a high-school enrichment program. Robotics, 10(1), 1–19. https://doi.org/10.3390/robotics10010013

102.

Vinall

Hellmich

E. A.

(2021). Down the rabbit hole: Machine translation, metaphor, and instructor identity and agency. Second Language Research & Practice, 2(1), 99–118.

103.

Waite

S. R.

(2021). Towards a theory of critical consciousness: A new direction for the development of instructional and supervisory leaders. Journal of Educational Supervision, 4(2), 65–79. https://doi.org/10.31045/jes.4.2.4

104.

Wang

(2025). Artificial intelligence in educational leadership: A symbiotic role of human-artificial intelligence decision-making. Journal of Educational Administration, 59(3), 256–270. https://doi.org/10.1108/JEA-10-2020-0216

105.

Wang

Pan

Miller

K. F.

Cortina

K. S.

(2014). Automatic classification of activities in classroom discourse. Computers & Education, 78(1), 115–123. https://doi.org/10.1016/j.compedu.2014.05.010

106.

Wardat

Tashtoush

M. A.

AlAli

Jarrah

M. J.

(2023). ChatGPT: A revolutionary tool for teaching and learning mathematics. Eurasia Journal of Mathematics, Science and Technology Education, 19(7), em2286. https://doi.org/10.29333/ejmste/13272

107.

Welborn

J. E.

Flores

III. (2024). Equity requires action: Principals’ use and value of culturally proficient educational practice. Journal of Critical Issues in Educational Practice, 13(1). Article 2.

108.

Willis

Ingle

W. K.

(2015). “Who Has Time for This?” negotiating roles in instructional supervision and evaluation. Journal of Cases in Educational Leadership, 18(1), 66–76. https://doi.org/10.1177/1555458914567665

109.

Woolverton

G. A.

Pollastri

A. R.

(2021). An exploration and critical examination of how “intelligent classroom technologies” can improve specific uses of direct student behavior observation methods. Educational Measurement: Issues and Practice, 40(3), 7–17. https://doi.org/10.1111/emip.12421

110.

Xie

(2023). Influence of AI-driven inquiry teaching on learning outcomes. International Journal of Emerging Technologies in Learning, 18(23), 59–70. https://doi.org/10.3991/ijet.v18i23.45473

111.

Yariv

(2009). The appraisal of teachers’ performance and its impact on the mutuality of principal-teacher emotions. School Leadership & Management, 29(5), 445–461. https://doi.org/10.1080/13632430903152302

112.

Zawacki-Richter

Marín

V. I.

Bond

Gouverneur

(2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1-27. https://doi.org/10.1186/s41239-019-0171-0

113.

Zepeda

S. J.

(2017). Instructional supervision: Applying tools and concepts (4th ed.). Routledge. https://doi.org/10.4324/9781315625874

114.

Zepeda

S. J.

Ponticell

J. A.

(1998). At cross-purposes: What do teachers need, want, and get from supervision? Journal of Curriculum and Supervision, 14(1), 68–87.

115.

Zepeda

S. J.

Ponticell

J. A.

(Eds.), (2019). The wiley handbook of educational supervision. Blackwell/Jon Wiley & Sons.

116.

Zuckerman

S. J.

O’Shea

(2020). Principals’ schema: Leadership philosophies and instructional leadership. Journal of School Leadership, 31(4), 274–296. https://doi.org/10.1177/1052684620966063

The Promises and Pitfalls of Artificial Intelligence in Instructional Leadership: A Focus on Teacher Supervision

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