Background: Dentistry is increasingly adopting AI technology; however, the preparedness of dental students for embracing the technology is still poorly known.
Objective: To examines the perception, attitude, and knowledge of the fourth-year dental students at Islamabad about the application of AI technology in dentistry and to evaluate the understanding of AI and its dental applications among final?year undergraduate dental students in a multi?center setting.
Methods: This cross-sectional survey was conducted from March to July 2025 including 165 Bachelor of Dental Surgery Final year students. After seeking ethical approval and informed consent, Participants were recruited using stratified random sampling with equal allocation from five dental institutions located in Islamabad. Data were collected using questionnaire consisting of 19 items, using a 5-point Likert scale. Statistical analysis was performed with SPSS version 26 including descriptive statistics and inferential tests i.e independent samples t?test (Welch's correction applied where variances were unequal) and one?way ANOVA. Effect sizes (Cohen's d) were calculated for significant t-test results. Assumptions of normality and homogeneity of variance were verified prior to ANOVA.
Results: 73.3% female participants showed strong familiarity with the AI concept (mean 4.36±0.87) but only moderate understanding of machine learning (3.36±1.09) and deep learning (3.27±1.10). Ethical concerns scored the lowest (2.98±1.02). Attitudes were predominantly positive; the highest agreement was for willingness to integrate AI into practice (3.95±0.81) and interest in AI training (3.92±0.80). Males demonstrated significantly higher total knowledge (23.25±2.94 vs 21.21±3.50, p=0.001) and more favorable attitudes (51.20±8.72 vs 48.46±5.73, p=0.047) than females. No statistically significant inter?institutional differences were observed.
Conclusion: Final?year dental students in Islamabad have basic knowledge and an optimistic attitude towards AI. However, deficiencies in technical and ethical knowledge, along with gender?related discrepancies, support the need to incorporate AI education into the dental curriculum.
M. Ali, Muhammad Talha, Sohaib Ahmad Zamir et al.· Proceedings· 0 citations
This study aims to investigate how artificial intelligence (AI) can enhance Lean Construction Management to achieve efficient and sustainable project delivery in Pakistan. It examines current practices, perceived benefits and the organizational, technical and cultural barriers that influence AI adoption within the construction sector.
A mixed-methods design was used, combining a systematic literature review, a Delphi-based expert consultation with 12 regional specialists, semi-structured interviews with 50 professionals and a quantitative survey of 125 valid responses. Statistical analyses, including correlation tests, were conducted to assess relationships between AI use, Lean outcomes and adoption factors.
Results reveal a strong positive correlation between AI tool usage and improved Lean outcomes, particularly in project planning, real-time monitoring and waste reduction. Predictive analytics and image-recognition technologies produce the greatest operational benefits. Despite growing interest, adoption remains limited due to high upfront costs, inadequate digital skills, data and integration challenges and organizational resistance to change. Capacity building and supportive policies are viewed as essential enablers.
This study provides the first empirical validation of AI-Lean integration in Pakistan’s construction sector. It demonstrates a strong positive correlation (r = 0.865) between AI tool usage and Lean outcomes, introduces a novel “fragmentation-of-adoption” concept specific to resource-constrained settings, and integrates the technology acceptance model, unified theory of acceptance and use of technology and diffusion of innovations theory into a unified adoption framework. These contributions offer a replicable evidence base for emerging economies, moving construction innovation literature beyond conceptual propositions to context-specific, empirically grounded practice.
M. Ali, Javed Ahmed Khan Tipu, E. Shaqour et al.· Construction Innovation· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.