LEADERSHIPCHALLENGESANDSTRATEGIESFORMANAGINGCHANGEINAI INFUSEDEDUCATIONALSYSTEMSINSOUTH-EASTNIGERIA.
Keywords:
Artificial Intelligence (AI), Leadership Strategies, Transformational Leadership, Resistance to Change, Organizational Change.Abstract
Abstract
This study aimed to identify and evaluate effective leadership strategies required for successfully
managing organizational change in educational institutions adopting AI technologies. The study was
guided by three research questions and two hypotheses. A descriptive correlational survey was used to
explore school leaders’ perceptions in South-East Nigeria. The population comprised 400
administrative leaders in the public and private University. The sample size for the study was 250
respondents made up of public and private university administrative leaders. The sample was drawn
using a stratified random sampling. A questionnaire, titled ‘’Leadership Strategies for Managing
Change in AI (LSMCAI) was developed for data collection. The content validation of the instrument
was determined by five educational technology experts (CVI = 0.92).The reliability of the instrument
was tested using Cronbach Alpha statistics. The overall coefficient index of (LSMCAI) was 0.84).
Data were collected electronically and analyzed using descriptive statistics, Pearson correlation and
linear regression at p < 0.05. Major findings revealed that major challenges include skill gaps,
resistance to change, and inadequate infrastructure; transformational and collaborative leadership
were most effective for fostering AI adoption; and leaders’ positive attitudes significantly predicted
implementation success. Transformational leadership correlated strongly with adoption success (r =
0.68, p < .01), supporting Ha₁, while Ha₂ was also supported by a significant regression coefficient (β
= 0.54, p < .001). Implications indicated that investing in AI-focused leadership training and
infrastructure can facilitate smoother change. Recommendations include embedding leadership
development and ethical governance into institutional AI strategies. One limitation is reliance on
self- reported perceptions. Further research should examine student outcomes in AI-infused systems.
