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Recent Submissions
The impact of using bionic font in speech-to-text Tools on the accuracy of English into Arabic interpretation /
Elhefny, Rawan Hesham,; Supervisor : Bahaa eddin M. Mazid, Ingy Farouk Emara. Includes Arabic Summary.
Consecutive interpreting (CI) requires significant cognitive effort, with interpreters juggling
listening, note-taking, and verbal output in real time. This study investigates whether the Bionic
Reading font, designed to enhance reading efficiency, could reduce cognitive load and improve
accuracy in Arabic CI tasks. It aims to determine whether the Bionic Reading font could optimize
the user experience of Speech-to-Text (STT) tools, leading to reduced cognitive load and improved
CI accuracy. A quasi-experimental design is used, with participants (n=5) engaging in an
interpreting task under both font conditions. Pre- and post-test questionnaires evaluates readability,
processing speed, comprehension, comfort, interpreting accuracy, cognitive load, and font
preference. Performance is further analyzed using Daniel Gile’s Effort Model, which provided a
framework for assessing cognitive strain, and through Errors, Omissions, and Infelicities (EOI)
analysis, which offers a systematic means of evaluating interpreting quality. The findings infoms
the development of user-friendly STT tools with customizable font options, offering practical
implications for enhancing communication accessibility and effectiveness, and laying the
groundwork for further exploration of this technology’s potential across languages.
An Open Architecture for Enterprise SD-WAN Networks /
Elwakil, Mohamed Ahmed Samy Mohamed,; Supervisor : Samy El-Hennawey, Ayman M. Bahaa-Eldin, Mohamed A. Sobh. Includes Arabic Summary.
Quality assessment of ChatGPT and Gemini English into Arabic translation in the domain of climate change : A comparative study /
El-Outify, Yara Tarek,; Supervisor : Mustafa Riad, Maha Fathi, Hanan Sharaf El-Dine. Includes Arabic Summary.
Fracture resistance of zirconia versus PEEK implant supported cantilever fixed partial denture with two span Lengths : (An in vitro study) /
Shedid, Roshan Mohamed Tarek,; Supervisor : Hesham Katamish, Rana Sherif. Includes Arabic Summary.
New methods for the selective determination of active pharmaceutical ingredients /
Saber, Yomna Ayman,; Suoervisor : Samy El- Sayed Sayed Ahmed Emara, Fotouh Mansour, Noha Ibrahim. Includes Ararbic and English Summary.
Digital radiographic assessment of the shaping ability of A contemporary rotary system in curved root canals using two different torque settings /
Helmy, Omar Mohamed Nader Hassan,; Supervisor : Nihal Ezzat Sabet, Mohamed Nabeel. Includes Arabic Summary.
Conventional versus digital measurements of marginal and internal fit of milled interim restorations :| (An In-Vitro study) /
El Attawy, Ahmed Mohamed,; Supervisor : Tamer Elhamy Shokry, Yara Sayed. Includes Arabic Summary.
Design, synthesis and biological evaluation of novel thiazolidinone derivatives as anti-cancer agents : (In vitro study) /
Kenawy, Omnia Gamal Mubarak,; Supervisor : Nahla Ahmed Hassan Farag, Khaled Omar Ahmed Mohamed, Mona Mohamed Abdelatty. Includes Ararbic Summary.
Impact of different torque setting on curved root canals geometry and cutting efficiency using two Ni-Ti rotary systems /
El-Gendy, Hesham Sobhi Hassan Said,; Supervisor : Abeer Hashem Mahran, Amira Galal. Includes Arabic Summary.
Machine translation output for Arabic-to-English translation of legal texts : A comparative study between AI Tools /
Reda, Dalia Ehab Abdulaziz,; Supervisor : Mustafa Riad, Sama Dawood. Includes Arabic Summary.
The effectiveness of machine translation (MT) in the legal domain requires close evaluation
using appropriate quality assessment models. This study assesses the translation quality of two
advanced MT systems, Gemini and ChatGPT by analyzing their English translations of three
Arabic Memorandums of Association. The TAUS Dynamic Quality Framework (DQF) was
adopted as the primary evaluation metric, with a focus on error typology and frequency to measure
translation performance. The research adopts a quantitative approach, examining the outputs in
terms of accuracy and fluency, while identifying and categorizing errors. A total of 1,022 errors
were recorded and analyzed: 425 in ChatGPT translations and 597 in Gemini. The findings indicate
distinct tendencies in each system: ChatGPT often omits source text content, while Gemini
exhibits a tendency toward over-translation. ChatGPT’s output showed a higher percentage of
accuracy-related errors, particularly mistranslations, whereas Gemini was more prone to over translation errors. The study underscores the ongoing necessity of human post-editing in legal
translation workflows. It also emphasizes the importance of incorporating domain-specific training
data and tailored quality assurance (QA) modules to improve MT output in legal contexts.
Ultimately, this research contributes to the growing body of literature on MT evaluation by
offering insight into the strengths, limitations, and error patterns of emerging AI-powered
translation tools. The researcher recommends further exploration of additional TAUS error
categories, such as style and locale, and calls for broader experimentation with other MT systems
to reflect the rapid development of AI in legal translation.
Keywords: Machine Translation, Legal Translation, Neural Machine Translation, TAUS Error
Typology, ChatGPT, Gemini.