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Communities of practice can help educational institutions develop ethical AI guidelines through discussions among educators, policymakers, and technology experts to identify potential risks and best practices. Communities of Practice can evaluate AI’s effects on equity and inclusion, ensuring that all students are treated fairly. It is important to engage stakeholders who have in-depth knowledge of the data and are directly impacted by AI decisions as this engagement helps ensure that students receive fair treatment

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.AI can improve leaders' data literacy by improving collections, analysis, and interpretation, thus providing expert knowledge in data analytics. Many educational leaders lack adequate training in data analytics, making AI's role in enhancing their data literacy and decision-making processes essential.AI can efficiently process vast amounts of data from various sources, breaking down data silos and providing timely recommendations for leaders DIDM.AI can assist education leaders in deciding on teacher hiring with efficiency and accuracy. AI can reduce our dependence on standardized test scores for teacher evaluation as these have been proven to be unreliable, biased, and inconsistent. Furthermore, AI supports personalized learning, and teachers' roles will evolve to focus more on coaching, mentoring, and developing students' social-emotional and cross-cultural skills.

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Moral values such as fairness and honesty in educational leadership can sometimes conflict with the use of AI-driven data-informed decisions. Neuroscience indicates that emotional engagement is essential in making ethical choices. Although AI can enhance efficiency and accuracy, it is unable to generate emotions such as empathy or guilt, which is essential in the moral decision-making process. Therefore, AI should complement, rather than replace human decision-making in education to ensure a compassionate and ethical approach

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The use of AI in educational leadership decision-making can amplify unconscious bias leading to discrimination against students in rural and urban classrooms. AI programs may perpetuate gender bias in hiring decisions favouring male candidates and penalizing resumes from women reflecting male dominance in the technology industry. In addition, facial recognition software used for school safety systems appears to be inaccurate at identifying people of colour, women, and children. Biases in AI can lead to discrimination against students in marginalized groups and students with special needs. Educational leaders must be vigilant in recognizing and addressing these biases to ensure their decision-making is fair and non-biased.

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Open discussions over the ethical implications and share other people's experiences

Develop shared values - look at creating a framework

Ongoing dialogue - foster an open culture of discussion to reflect and drive continuous improvement

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Personalised Learning - resources, assessments, lesson plans, adaptive teaching strategies

Admin tasks - automation

Data Analysis - looking at trend and patterns. Identify potential pitfalls and future issues

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Relates back to Privacy and GDPR too. Is it ethical for us to share sensitive data with AI and us not really know where it is going or who has access to it? Not all Gen-AI is GDPR compliant. Co-pilot through Microsoft Office is though...

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free up time for other tasks- generate ideas/case studies/things you may not think about. Identify learners at risk of dropping off the course

Can you use as a tool to collect data and analyse but not solely reliable!

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Rachel and Tony - Time saving for hiring, stats for dropping out of school. Education on how to use AI responsibly. Assist teacher retention

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Bias - could have unfair outcomes based on the data they are trained on.

Transparency - we don't necessarily understand how the AI makes the judgements

Over-reliance - to save time as we are all time poor

GDPR - is it a secure? Can we guarantee the data is safe?

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In educational leaders’ AI-assisted DIDM, AI may bias against students from minorities and from low socioeconomic families, and students with special needs. AI may also have racial and gender biases in teacher/staff hiring and over shared decision-making processes (Herold & Schwartz, 2017). Data gathered using AI could be scrutinised by leaders, staff and communities in order to ensure it is not biased. Any decisions should ultimately lie with the educational leaders who should not rely solely on AI recommendations.

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AI can increase efficiency and accuracy in educational leaders’ decision making, however, data can only inform, but “never completely drive decisions” (Schildkamp, Poortman, Ebbeler, & Pieters, 2019, p. 284). Using a probabilistic approach, AI algorithms generate useful information and provide recommendations for leaders’ decisions. Deep learning (which analyse the neural networks efficiently) can now outperform humans in identifying faces, recognising speech, and many other tasks. AI relies on collating large amounts of data, as such, this data must be stored and used ethically.

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AI can assist educational leaders in

teacher hiring by predicting the effectiveness and potential turnover

before a teacher steps foot into a classroom.

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AI can increase efficiency and accuracy in educational leaders’ data driven decision making. AI can assist with personalised learning. AI can provide the

leaders with expert knowledge in data analytics, which allows leaders to make data-informed decisions. As a school's progress can be measured by data, and schools are dealing with much more data than previously, leaders can use AI at analyse more effectively. AI can predict students at risk of dropping out and early interventions can be put in place. AI can potentially assist educational leaders in making decisions on teacher hiring with efficiency and accuracy. AI can assist with assessment in that large amounts of data from observations in class and formative tasks can be used instead of a single test.

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Leaders need to understand how AI works, identify as well as recognize the limitations of AI. Large amounts of data need to stored for AI to be effective meaning that a lot of personal details may be stored, which will require extra security protocols. Bias is prevalent in AI and any results should be scrutinized. There are also risks regarding emotional responses e.g. AI dos not generate emotions over the decision making processes and will lack empathy. Leaders need to be transparent in how AI is used in schools.