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This study analyzed how active Twitter users who are also school leaders used the tool. The researchers collaboratively identified K-12 school leaders who were active on Twitter and then collected tweets from those school leaders. The final sample for this study included 115 Twitter users and over 180,000 tweets from those individuals. The results of this study indicated that school leaders overwhelmingly used Twitter for educational purposes and as a way to create communities of practice focused on educational issues.

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This study investigated the relationship between the leader's gen der, leadership position, Twitter use, and influence on information diffusion in the communication network on Twitter. We collected the 30,200 latest tweets of 151 active Twitter users who held educational leadership positions. Results of social network analysis and multiple regression analyses suggest a gender inequality in the leader's influence on information diffusion in the network. Find ings also indicate no significant relationship between leadership position (district vs. building) and a leader's influence in the network. Moreover, Twitter following was positively associated with the leader's influence in the network, whereas the number of followers, weekly tweets, and the time of Twitter account creation did not predict the leader's influence. Practical implications on how leaders use Twit ter to disseminate information are discussed.

Superintendents’ instructional leadership is critical to the academic success of school systems. In addition to traditional work complexities, today’s superintendents must navigate rapid and significant technological transformations. In this study, an exploratory sample of “technology-savvy” superintendents was interviewed to ascertain advice about how to navigate the complexities that surround successful district-level technology leadership strategies and mind-sets. Participants highlighted issues such as budgets, professional development, and instructional leadership, and affirmed the value of personally modeling technology use. They also emphasized both personal and organizational risk-taking and shared how communities of practice can help alleviate skill and knowledge gaps.

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PurposeThe purpose of this study is to examine school leaders' experiences with adoption and implementation of artificial intelligence systems in their schools. It examined the factors that led educational administrators to adopt one artificial intelligence program (ALEKS) and their perceptions around the implementation process.Design/methodology/approachThis was a qualitative case study that included structured interviews with seven individuals who have adopted artificial intelligence programs in their schools. Participants were identified through purposive and snowball sampling. Interview transcripts were analyzed and put through a two-step coding process involving in vivo coding as well as pattern coding.FindingsTwo major themes emerged from this study pertaining to the state of the diffusion of artificial intelligence through the adoption and implementation process. The findings indicated that school leaders were actively engaged in conversations related to AI adoption and implementation. They also created structures (organizational) to ensure the successful adoption and implementation of artificial intelligence.Originality/valueThis is an original study that examined the experiences of school leaders who have adopted and implemented artificial intelligence in their schools. The body of literature related to artificial intelligence and school leadership is extremely limited.

Although school 1:1 computing programs have been around for over 20 years, the literature base on such programs remains sparse. Despite limited research on efficacy and implementation, 1:1 initiatives are increasingly popular in P-12 schools. The impact of these programs is not yet certain. The purpose of this study was to determine the impact that 1:1 schools had on teachers' technology competency and instructional integration. Propensity score matching was used in order to have similar control (non-1:1) and treatment (1:1) schools in the sample. The final sample included 110 high schools and 922 secondary school teachers in the state of Iowa. Data were collected from public databases as well as from teacher surveys. The final data analysis was conducted using a multilevel model with three separate models for each of the dependent variables (competency and integration). Results indicated that teachers in schools with 1:1 student computing initiatives report higher levels of personal technology competency and classroom integration of learning technologies.