Department of English
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Item Lost in AI: Analyzing Students' Linguistic Agency and the Usage of Artificially Generated Writing Tools?(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) IRAM HASSAN; CIIT/SP24-MEL-005/LHR; Dr. Javaria Farooqui; LHR TP 10050This study explores the impact of Artificial Intelligence (hitherto referred to as Al) generated writing tools on students' confidence, independence, and linguistic agency in academic writing, and examines how teachers' evaluation strategies of Al-generated content shape students' writing behavior. The increasing prevalence of AI tools (ChatGPT, Grammarly and QuillBot), required an exploration of their impacts on student's learning experiences, perceptions of authorship, and assessment processes. This study employed a mixed-methods design that included quantitative surveys with 200 university students and 50 university professors, as well as qualitative design to analyze the open-ended responses. Linguistic Agency Theory and Formative Assessment Theory is used in this study to illustrate how students employ control over their written work and how teacher's feedback influences student learning outcomes while using Al. The findings of this study shows that frequent use of Al tools improves students' self-confidence, and increase writing fluency, especially grammar as they possessed varying levels of independence and linguistic agency. While some were able to evaluate Al suggestions critically and maintain their voice, others relied on Al too heavily. Educators can support or hinder student agency based on how they provide feedback to students. Ethical issues regarding originality, academic integrity were identified in this study, particularly in Pakistani context, and multilingual higher education. This study extends our understanding of how AI contributes in academic writing, and also provide practical suggestions for ethical usage policies, and evaluation practices with technological support. These findings are helpful for educators, policymakers, and institutions with the goal of supporting the integration of Al tools into the educational environment and to enhance student learning experiences while preserving students' critical thinking skills and authorship.Item A Comparative Study of DeepSeek R1 (Reasoning) and GPT-4.0 (Non-Reasoning) Agentic AI Models for Irony and Sarcasm Detection in Urdu and English(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Samra Hameed; CIIT/SP24-MEL-009/LHR; Dr. Saima Akhtar; LHR TP 10053Artificial Intelligence (AI) systems often face difficulties in accurately identifying irony and sarcasm because these forms of figurative language convey meanings that differ from their literal expressions. Successful detection of such linguistic phenomena requires AI models to possess contextual understanding, cultural awareness, and the ability to interpret subtle linguistic cues. English is considered a high-resource language with extensive training data available for AI systems, whereas Urdu is a comparatively low-resource language with limited datasets. As a result, AI models generally perform better in English than in Urdu. Detecting sarcasm and irony across both languages remains challenging due to the linguistic and cultural complexities associated with Urdu. Furthermore, limited research has been conducted on comparing the performance of reasoning and non-reasoning AI models in this domain. This study presents a comparative analysis of ChatGPT-4.0, a non-reasoning agentic AI model, and DeepSeek R1, a reasoning-based AI model, to evaluate their effectiveness in detecting irony and sarcasm in English and Urdu texts. A mixed-methods research design was adopted, combining quantitative performance evaluation with qualitative error analysis. The study utilized a total of 10,000 text samples, comprising 5,000 English and 5,000 Urdu instances. The English dataset included sarcastic and non-sarcastic content collected from Twitter and Reddit, while the Urdu dataset was based on the Urdu Sarcastic Tweets (UST) corpus and additional Urdu-language data. By comparing the performance of the two AI models across both languages, the study aims to provide insights into the strengths and limitations of reasoning and non-reasoning approaches in multilingual sarcasm and irony detection. The findings contribute to the growing field of Natural Language Processing (NLP) and highlight the challenges and opportunities associated with developing AI systems for low-resource languages such as Urdu.Item Gender and Morality: An Analysis of Social Constraints and Personal Freedom in The Woman in White by Wilkie Collins(2025) Muhammad Zia Ul Haq; CIIT/FA22-REL-013/LHR; Dr. Memoona Idris; LHR TP 10061This thesis examines the complex relationship between gender, morality, and personal freedom in Wilkie Collins’s The Woman in White within the socio-cultural context of Victorian England. Drawing primarily on Catherine Belsey’s poststructuralist approach to textual analysis, the study treats the novel not as a transparent reflection of reality but as an interrogative text that exposes ideological contradictions embedded in language, narrative form, and representation. Through close textual analysis of selected passages, the research explores how dominant Victorian ideologies—particularly patriarchy, gender norms, legality, sanity, and respectability—construct and regulate moral subjectivities, especially those of women. The thesis further incorporates utilitarian moral theory to evaluate the ethical decisions and behaviors of key characters, highlighting how utilitarian logic is selectively employed or distorted to justify oppression, control, and self-interest. Female characters such as Laura Fairlie, Marian Halcombe, and Anne Catherick are analyzed as sites where moral conflict and social constraint intersect, revealing the tension between imposed virtue and individual agency. Male authority figures, notably Sir Percival Glyde and Count Fosco, are examined as embodiments of patriarchal power and moral relativism. By foregrounding narrative multiplicity, silences, and contradictions, this study demonstrates how The Woman in White critiques Victorian moral absolutism while anticipating modern debates on gender, ethics, and freedom. Ultimately, the thesis argues that Collins’s novel remains a socially resonant text that challenges rigid moral frameworks and exposes the enduring legacy of gendered injustice.Item Impact of Automated Written Evaluated (AWE) Feedback on English Writing Accuracy of ESL University Students of Pakistan.(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Shiza Azam; CIIT/SP23-REL-022/LHR; Dr. Aasia Nusrat; LHR TP 10062Regardless of a lot of researches on use of Automated Written Evaluation (AWE) on students of English as a Second Language (ESL), this Quasi Experimental study examines the long term impact of Automated Written Evaluation Feedback (AWEF) on ESL students of Pakistani University. This study employed this technique because of its significance in examining the impact of AWE and with this design; this study explored how AWE with Grammarly helps students in improving English writing quality. Three testing occasions including Pre-Test, Post- Test and Delayed Post-Test were employed to examine the long term relative efficacy of AWE and Teacher Feedback. Over the course of semester, 60 students were divided into two groups: Experimental and Control group and were provided AWE feedback and Teacher feedback relatively. The study used quantitative techniques including t-test and ANOVA and focused on particular errors categories comprising Subject Verb Agreement, Article Usage and Spelling. The results of the post test showed that both groups, AWE and Teacher Feedback, contributed in short term improvement but the results of delayed post-test revealed that the students from the treatment group retained the improvement and showed a statistically notable improvement. This study advances the understanding of technological use in language acquisition and also pedagogically raises the prospect of using AWE along with teacher feedback to foster learning using limited resources. This study was concluded with discussion and limitations of this study.Item Evaluating AI’s Effectiveness in Content Analysis: A Linguistic Comparison of Automated and Human Text Interpretation in the Aurat March Discourse on Social Media(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Esha Shakoor; CIIT/SP24-MEL-003/LHR; Dr. Javaria Farooqui; LHR TP 10048This study evaluates the effectiveness of AI in analyzing the emotional, cultural, and ideological aspects of feminist discourse in Pakistan. It compares human and AI interpretations of online discussions related to the Aurat March on Facebook, Instagram, and YouTube. Using qualitative methodology supported by relational content analysis (RCA) and Technofeminism (Wajcman, 2004) as a theoretical framework, the research examines how human and the ChatGPT-5 model interpret emotional tone, sarcasm and moral framing in multilingual comments written in English, Urdu and Roman Urdu. The results shows that while the AI efficiently detects explicit emotions in English, it fails in understanding sarcasm, irony and moral expressions embedded in Urdu and Roman Urdu. AI misinterpreted phrases with cultural and religious connotations, such as “khuda ka wasta hai” or “drama karna,” while human interpreters recognized their emotional and contextual depth. This shows the limitations of AI's ability to deal with culturally specific discourse. The study supports Technofeminist arguments that technology reflects the social and gender biases of its developers. The gap in AI highlights the persistence of algorithmic bias and the dominance of Western linguistic frameworks in language models. This research contributes to studies of feminist discourse and AI ethics by proposing a hybrid model that integrates human interpretation with AI-assisted sentiment analysis. This approach combines the efficiency of AI with the sensitivity of human understanding of context. The study concludes that although AI can quickly process large data sets, it cannot yet replace human understanding when analyzing emotionally and culturally complex feminist communication. The study calls for the development of AI systems that take into account cultural, gender and context aspects, and are able to interpret various linguistic facts in digital spaces.Item Language, Nature, and the Framing of Death: An Ecolinguistic Analysis of Muneer Niazi’s Taiz Hawa Aur Tanha Phool(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Amina Arif; CIIT/SP24-MEL-014/LHR; Dr. Aasia Nusrat; LHR TP 10058This thesis explores how Urdu lyric poetry, in this case, Taiz Hawa Aur Tanha Phool by Muneer Niazi (1994), is important in solving the global environmental crisis by promoting localized environmental ethics in Pakistan. The study uses the Ecological Discourse Analysis (EDA) according to the six-part framework of Arran Stibbe (Identity, Ideology, Salience, Metaphor, Conviction, and Erasure) to systematically deconstruct the way Niazi uses language as a positive, ecocentric discourse. The study is based on a qualitative, constructivist-interpretivist design, which examines twenty purposively sampled poems to fulfill two main goals: to examine how identity, ideology, and salience facilitate eco-consciousness, and to examine how metaphor, conviction, and erasure re-brand human mortality as an ecological flow. The main thesis is that Niazi manages to create an ecocentric worldview through the active agency of non-human things (challenging erasure), the creation of strong cyclical metaphors, and the creation of a moral belief in interdependence. This discussion moves the study of Niazi beyond conventional social interpretations, using a global approach to non-Western lyrical poetry and providing a resource base to South Asian ecocriticism and environmental communication.Item The Erasure of Nature in Corporate Greenwashing: An Ecolinguistic Analysis of Environmental Claims in Multinational Advertisements in Pakistan.(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Ajwa Sarosh Azhar; CIIT/SP24-MEL-002/LHR; Dr. Aasia Nusrat; LHR TP 10047This study aims to explore how multinational companies market environmental sustainability through "greenwashing" their marketing communication. It focuses on how these companies make "green" claims using linguistic techniques to trick consumers into thinking they are environmentally responsible. The researcher has selected twelve advertisements from an intentional sample of eight multinational companies. Dove, Pond's, Hemani Herbal, Garnier, Ariel, Surf Excel, Bonus, and Express Power. To better understand the issue, a mixed-method approach has been used for this study. The qualitative analysis was conducted through Stibbes Ecolinguistic analysis of the advertisements from the eight selected companies. The quantitative analysis was conducted through a closed-ended Likert-type survey that was given to 200 participants. The SPSS software was then used to analyze the data generated from the quantitative analyses. Qualitative analysis discovered that all of the companies assessed were using language such as "chemical-free," "pure," "organic," and "all-natural." The use of metaphorical language (cleanliness, purity, natural harmony) in the advertisements further reinforced these misleading bio facts. Results of the quantitative analysis showed that participants were aware of certain corporate practices of exploiting consumers through greenwashing; however, they were still impacted by the eco-linguistic cues employed in the advertisements. In summary, this study found that multinational corporations used the linguistic and symbolic tools at their disposal to create an environmentally friendly narrative that influences consumer perception, while simultaneously hiding their lack of true environmentally sustainable endeavor. This study has shown that, in order to establish stronger regulatory control over and enhance the integrity of the way advertising is performed in Pakistan, greater transparency in the methods of communicating an environmental message will need to be established in the marketplaceItem The Role of Holy Pixels in Shaping Children’s Religious Identity and Gender Roles: Analysis of Kaneez Fatima and Ghulam Rasool Animated Series(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Warda Zulfiqar; CIIT/SP24-MEL-015/LHR; Dr. Rai Zahoor Ahmed; LHR TP 10059This study analyzes the role of animated series in shaping children’s religious identity and gender roles through a qualitative Corpus Assisted Discourse Analysis (CADS) of YouTube series ‘Kaneez Fatima and Ghulam Rasool’ produced by Dawat-e-Islami. The study is grounded in two theories; Gee’s theory of discourse which views language as a social practice through which identities, roles, and values are enacted and Gee’s Analytical lens of identity, and Bandura’s Social Learning Theory, which emphasizes learning through observation, imitation, and modeling. The study aimed to analyze the representation of religious and gender identity by Muslim cartoon characters that serves as symbolic models for audience children to follow and imitate (SLT). Data for the study were comprised on transcripts of cartoon episodes’ that were collected from three YouTube channels—Kids Land, Madani Channel English, and Dawat-e-Islami English—using purposive sampling. The corpus was compiled in .txt files, comprised on 29,461 words, including 16,037 words from ‘Kaneez Fatima’ series and 13,424 words from ‘Ghulam Rasool’ series. By using Sketch Engine, the data were analyzed through Corpus-Assisted Discourse Analysis (CADS), followed by corpus-driven thematic interpretation. Findings of the study reveal repeated usage of Islamic vocabulary, practices and rituals, that constructs religious identity by positioning children within Islamic moral and ritual frameworks, exemplifying the Prophet Muhammad (S.A.W) as a role model. Similarly, through distinct lexical patterns found in both series represent gender roles: female- centered discourse emphasizes domesticity, modesty, nurturing, and beautification (e.g., “kitchen,” “pardah,” “scarf”, “softness,” “makeup”, “bangles”), while male-centered discourse highlights leadership, strength, moral responsibility, and religious authority (e.g., “sacrifice,” “prophethood,” “power,” “fight”). These findings support the view that children’s media transmits moral and religious values through revisiting core ideas in repeated cycles and socially meaningful discourse.Item Syntax-driven Sentence Simplification: A Computational linguistics Approach to enhance text Readability for Non-native English Speakers(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Aleena Rashid; CIIT/SP24-MEL-013/LHR; Dr. Saima Akhtar; LHR TP 10057This thesis presents a comprehensive investigation into syntax-driven sentence simplification as a computational linguistics approach to enhance text readability for non-native English speakers. Drawing upon established research in syntactic simplification and text cohesion (Siddharthan, 2006), this study develops a rule-based text simplification system that targets complex syntactic structures, particularly relative clauses and coordinate structures. The research methodology employs dependency parsing using SpaCy v3.5 to identify and transform syntactic patterns that pose comprehension challenges for non-native speakers. The system implements transformation rules based on syntactic simplification principles established by Chandrasekar et al. (1996) and extends the work of Medero and Ostendorf on identifying targets for syntactic simplification. The evaluation framework incorporates SARI (System for Assessing Text Simplification) metrics as proposed by Xu et al. (2016), alongside human evaluation protocols to assess simplification quality, meaning preservation, and grammaticality. Results demonstrate significant improvements in text readability while maintaining semantic coherence through the application of rhetorical structure theory and centering theory principles. The study contributes to the field of computational linguistics by providing a systematic approach to syntactic simplification that addresses the specific needs of non-native English speakers in educational contexts.Item Enhancing Multimodal Sentiment Analysis for Urdu Using Cross Modal Transfer Learning(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Uzair Majeed; CIIT/SP24-MEL-008/LHR; Dr. Saima Akhtar; LHR TP 10052Urdu, which is spoken by more than 230 million people around the world, is also a case in point, since it is severely underrepresented in the field of natural language processing research. Most of the existing systems used for Urdu sentiment analysis rely solely on the text, overlooks the rich emotional indications in speech e.g., tone, emphasis and prosody. This gap and the inadequacy of datasets or complex code-switching patterns between Urdu and English combined has restricted the accuracy of sentiment analysis in this low-resource language. This research involved creation of multimodal sentiment analysis framework for Urdu by combining text and audio using cross modal transfer learning. We built a dataset of 10,847 aligned text-audio pairs obtained from Facebook, Twitter and from original recordings and annotated in the positive, negative and neutral categories with high overlap between annotators (Cohen's kappa = 0.78). The framework uses multilingual BERT (mBERT) for the encoding of the text and convolutional neural networks for audio feature extraction. An attention based fusion mechanism is a dynamic mechanism that computes weight for modality contribution dependent upon the signal quality. Cross-modality transfer learning - through contrastive loss and knowledge distillation - allows conducting knowledge transfer across modalities. The proposed framework was able to achieve 84.7% accuracy and 83.2% macro F1-score on the held out test set. And it stood a good 21.8% relative improvement from text only mBERT (79.2%) and also 24.0% improvement over traditional SVM baselines (68.3%). Ablation studies validated that cross-lingual transfer had a positive effect of 8.4 percentage points while multimodal integration had a positive effect of 5.5 points and attention-based fusion +3.4 points over early fusion. The model is quite good at processing code-switched content (83.1% accuracy for instances mixing Urdu and English) and low demographic bias with respect to age, gender and regional variations. These findings show that multimodal strategies with transfer learning can significantly reduce the gap in performance for high-resource and low-resource languages. The dataset and methodology provide zero foundation for imitating Urdu NLP held in future to be engaged within research and practical applications in social media monitoring and customer feedback.