Impact of AI technology adoption on workload management: A pilot study with special references to private university lecturers in Colombo district

Abstract

Artificial intelligence is becoming increasingly common in higher education, changing how academic staff handle teaching and administrative work. However, very few studies have examined its effect on instructors at private universities in the Colombo District, particularly the possibility that AI could add to their workload through extra learning and verification demands. Therefore, this pilot study examines the impact of AI adoption, specifically AI-enabled task automation, AI-assisted content creation, and digital communication tool integration, on lecturer workload management, weighing its benefits and drawbacks. Following a positivist philosophy and a deductive approach, data were collected from 15 private university lecturers in the Colombo district using a 30-item, 5-point Likert scale questionnaire distributed through Google Forms, selected via simple random sampling, and analysed in SPSS through reliability testing, correlation, and multiple regression. All three independent variables correlated significantly and positively with workload management (r = .626–.829, p < .05) and together explained 75.8% of the variance (adjusted R² = .693, F(3,11) = 11.515, p = .001). Content creation emerged as the strongest and only statistically significant unique predictor (β = .572, p = .023), while task automation (p = .649) and digital communication (p = .136) were weaker, likely because of the overlap between the constructs rather than multicollinearity (VIF = 1.73–2.13). This indicates that H2 was fully supported, whereas H1 and H3 were only partially supported. Given the small pilot sample, single-campus context, age concentration, and some departure from residual normality, these findings are preliminary and specific to the lecturers in Colombo district. However, they still indicate that AI adoption, particularly through content creation tools, meaningfully supports workload management, offering an early contribution to how technology adoption in Sri Lankan higher education is understood, and suggesting that private universities should prioritise AI training in content creation and communication tools while larger, multi-campus research is conducted to confirm how well these results generalise.