Abdullah Al Jubair
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Abdullah Al Jubair has a total of 41 co-authors. Here are the top co-authors by number of shared publications:
- Kishor Morol
Department of Computing and Information Science, Cornell University, New York, USA
5 shared publications - Muhammad Firoz Mridha
Department of Computer Science and Engineering, American International University-Bangladesh, Dhaka, Bangladesh
4 shared publications - Ahmed Shakib Reza
Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh
3 shared publications - Shakib Sadat Shanto
Department of Computer Science, American International University-Bangladesh (AIUB), Dhaka, Bangladesh
3 shared publications - Zishan Ahmed
Department of Computer Science, American International University-Bangladesh (AIUB), Dhaka, Bangladesh
3 shared publications - Nafiz Fahad
Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia
2 shared publications
Latest Publications
- Open Access
- 1 April 2026
- Published by Institute of Advanced Engineering and Science in International Journal of Electrical and Computer Engineering (IJECE)
- Vol. 16 (2) , 924-944
The ethics of AI technology in academic work: assessing the line between assistance and plagiarism
The integration of artificial intelligence (AI) into academia has transformed educational practices and enhanced personalized learning and problem-solving capabilities. However, this raises significant ethical concerns regarding the balance between legitimate assistance and plagiarism. This study investigated public perceptions of AI in academic settings, focusing on its impact on effectiveness, dependency, and ethical considerations of AI use. A survey of 498 respondents from various educational roles was conducted, and the data were analyzed using SPSS for descriptive statistics, chi-square tests, and regression analyses. The results identified a significant correlation between people’s educational roles and their interaction with AI tools (χ2(6) = 16.488, p = 0.036), reflecting the diverse patterns of interaction within the academic community. More frequent use of AI was linked to less dependency (β = −0.298, p < 0.001), contradicting the widespread belief of over-reliance on AI. Age and educational role had limited explanatory value in perception of AI dependency issues (R2 = 0.033). The findings indicate a strong correlation between AI usage frequency and dependency levels, with increased exposure to AI fostering a more critical approach rather than a dependent one. Concerns regarding the unethical use of AI, inaccuracies in AI-generated content, and the need for clear institutional policies were also highlighted. This study underscores the importance of responsible AI integration, advocating for ethical frameworks and educational interventions to ensure that AI enhances learning without compromising academic integrity.
- 1 April 2026
- Published by Institute of Advanced Engineering and Science in International Journal of Electrical and Computer Engineering (IJECE)
- Vol. 16 (2) , 924-944
Open AccessThe ethics of AI technology in academic work: assessing the line between assistance and plagiarism
The integration of artificial intelligence (AI) into academia has transformed educational practices and enhanced personalized learning and problem-solving capabilities. However, this raises significant ethical concerns regarding the balance between legitimate assistance and plagiarism. This study investigated public perceptions of AI in academic settings, focusing on its impact on effectiveness, dependency, and ethical considerations of AI use. A survey of 498 respondents from various educational roles was conducted, and the data were analyzed using SPSS for descriptive statistics, chi-square tests, and regression analyses. The results identified a significant correlation between people’s educational roles and their interaction with AI tools (χ2(6) = 16.488, p = 0.036), reflecting the diverse patterns of interaction within the academic community. More frequent use of AI was linked to less dependency (β = −0.298, p < 0.001), contradicting the widespread belief of over-reliance on AI. Age and educational role had limited explanatory value in perception of AI dependency issues (R2 = 0.033). The findings indicate a strong correlation between AI usage frequency and dependency levels, with increased exposure to AI fostering a more critical approach rather than a dependent one. Concerns regarding the unethical use of AI, inaccuracies in AI-generated content, and the need for clear institutional policies were also highlighted. This study underscores the importance of responsible AI integration, advocating for ethical frameworks and educational interventions to ensure that AI enhances learning without compromising academic integrity.
- Open Access
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 882-888
IMVB7t: A Multi-Modal Model for Food Preferences based on Artificially Produced Traits
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 882-888
Open AccessIMVB7t: A Multi-Modal Model for Food Preferences based on Artificially Produced Traits
- Open Access
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 889-895
Assessing Participants Engagement in Virtual Meetings by Using Facial Landmarks and Deep Learning
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 889-895
Open AccessAssessing Participants Engagement in Virtual Meetings by Using Facial Landmarks and Deep Learning
- Open Access
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 1007-1013
MIC: Medical Image Classification Using Chest X-ray (COVID-19 & Pneumonia) Dataset with the Help of CNN and Customized CNN
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 1007-1013
Open AccessMIC: Medical Image Classification Using Chest X-ray (COVID-19 & Pneumonia) Dataset with the Help of CNN and Customized CNN
- Open Access
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 87-92
Rokkha: Enabling Offline Emergency Location Sharing via SMS
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 87-92
Open AccessRokkha: Enabling Offline Emergency Location Sharing via SMS
- Open Access
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 719-724
Optimized Malaria Identification through Transfer Learning Appoach
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 719-724
Open AccessOptimized Malaria Identification through Transfer Learning Appoach
- Open Access
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 295-302
Towards Interpretable Emotion Classification in Bangla: A Hybrid Deep Learning Approach with Explainable AI
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 295-302
Open AccessTowards Interpretable Emotion Classification in Bangla: A Hybrid Deep Learning Approach with Explainable AI
- Open Access
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 540-547
Machine Learning for Predictive Database Caching Strategies: A state-of-the-art review
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 540-547
Open AccessMachine Learning for Predictive Database Caching Strategies: A state-of-the-art review
- Open Access
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 303-310
Explainable English Hate Speech Detection: A Custom BiLSTM Model with LIME for Multilabel Classification
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 303-310
Open AccessExplainable English Hate Speech Detection: A Custom BiLSTM Model with LIME for Multilabel Classification
- Open Access
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 311-318
Acoustic Analysis of Speech for Gender and Age Classification Using CNN and Machine Learning Techniques
- 17 October 2024
- Published by Association for Computing Machinery (ACM)
- p. 311-318
Open AccessAcoustic Analysis of Speech for Gender and Age Classification Using CNN and Machine Learning Techniques