arXiv subjects

Muhammad Sajjad Akbar

Publications and source records attributed to Muhammad Sajjad Akbar.

3 recordsLinked to original sources

arXiv2026

AI and Authenticity in Islamic Research: A Critical Evaluation of Generative AI Reliability, Hallucination, and Source Fidelity in Quranic, Hadith, and Fiqh Knowledge

Generative Artificial Intelligence (AI) is increasingly used by Muslims for religious guidance, Qur'anic interpretation, Hadith explanation, jurisprudential rulings, and Islamic education. Despite its growing adoption, there is limited empirical evidence on whether current AI systems provide authentic, verifiable, and trustworthy Islamic knowledge suitable for high-trust religious contexts. This study evaluates six leading generative AI systems using fifty realistic open-ended Islamic questions covering Qur'anic interpretation, Hadith, Fiqh, ethics, pastoral advice, and Madhhab-sensitive topics. Responses were collected under real-world conditions from participants in Australia and the United Kingdom and analysed using a mixed-method framework examining domain accuracy, citation verification, hallucinations, jurisprudential consistency, uncertainty handling, source provenance, and geographical variation. The study addresses four research questions: (1) How accurate and authentic are AI-generated responses across major Islamic knowledge domains? (2) To what extent do AI systems produce hallucinations, incomplete citations, or unverifiable religious references? (3) How consistently do models handle jurisprudential disagreement, Madhhab diversity, and uncertainty? (4) Are current AI systems sufficiently reliable for religious guidance, Islamic education, and scholarly research? Overall, current generative AI systems are valuable as assistive tools for introductory Islamic learning but should not be treated as authoritative sources for religious rulings or Islamic research without verification against authenticated primary sources and qualified scholarly expertise. This study provides one of the first comprehensive empirical evaluations of AI reliability within Islamic knowledge, offering practical guidance for researchers, educators, AI developers, and the wider Muslim community.

cs.AI↗

arXiv2025

Beyond Detection: Designing AI-Resilient Assessments with Automated Feedback Tool to Foster Critical Thinking

The growing use of generative AI tools like ChatGPT has raised urgent concerns about their impact on student learning, particularly the potential erosion of critical thinking and creativity. As students increasingly turn to these tools to complete assessments, foundational cognitive skills are at risk of being bypassed, challenging the integrity of higher education and the authenticity of student work. Existing AI-generated text detection tools are inadequate; they produce unreliable outputs and are prone to both false positives and false negatives, especially when students apply paraphrasing, translation, or rewording. These systems rely on shallow statistical patterns rather than true contextual or semantic understanding, making them unsuitable as definitive indicators of AI misuse. In response, this research proposes a proactive, AI-resilient solution based on assessment design rather than detection. It introduces a web-based Python tool that integrates Bloom's Taxonomy with advanced natural language processing techniques including GPT-3.5 Turbo, BERT-based semantic similarity, and TF-IDF metrics to evaluate the AI-solvability of assessment tasks. By analyzing surface-level and semantic features, the tool helps educators determine whether a task targets lower-order thinking such as recall and summarization or higher-order skills such as analysis, evaluation, and creation, which are more resistant to AI automation. This framework empowers educators to design cognitively demanding, AI-resistant assessments that promote originality, critical thinking, and fairness. It offers a sustainable, pedagogically sound strategy to foster authentic learning and uphold academic standards in the age of AI.

cs.CY↗

arXiv2022

6G Survey on Challenges, Requirements, Applications, Key Enabling Technologies, Use Cases, AI integration issues and Security aspects

Fifth-generation (5G) wireless networks will likely offer high data rates, increased reliability, and low delay for mobile, personal, and local area networks. Along with the rapid growth of smart wireless sensing and communication technologies, data traffic has increased significantly and existing 5G networks are not able to fully support future massive data traffic for services, storage, and processing. To meet the challenges ahead, research communities and industry are exploring the sixth generation (6G) Terahertz-based wireless network that is expected to be offered to industrial users in just ten years. Gaining knowledge and understanding of the different challenges and facets of 6G is crucial in meeting the requirements of future communication and addressing evolving quality of service (QoS) demands. This survey provides a comprehensive examination of specifications, requirements, applications, and enabling technologies related to 6G. It covers disruptive and innovative, integration of 6G with advanced architectures and networks such as software-defined networks (SDN), network functions virtualization (NFV), Cloud/Fog computing, and Artificial Intelligence (AI) oriented technologies. The survey also addresses privacy and security concerns and provides potential futuristic use cases such as virtual reality, smart healthcare, and Industry 5.0. Furthermore, it identifies the current challenges and outlines future research directions to facilitate the deployment of 6G networks.

cs.NI↗