Academic Assignments Research Articles

Ethical and effortful: workshopping human and generative AI academic writing collaborations

The launch of Open AI’s ChatGPT in 2022 caused a furore within higher education. While initial reactions were negative – educators imagined the end of the undergraduate essay and an acceleration in academic integrity departures – more recent conversations have emphasised how these tools might enhance teaching and learning experiences. This paper explores one possibility for approaching student use of generative artificial intelligence (GenAI) tools, by considering their use in relation to academic skill development. It focuses on a set of workshops conducted within a graduate professional development course at Queen’s University (Canada) in early 2024. The first workshop examined commonalities in Western, English academic writing structures; identified how demystifying these structures supports academic writing and reading practices; and considered how GenAI tools that utilise large language models (LLMs) mimic these structures to enhance students’ awareness of GenAI’s potential applications and limitations, and to identify the processes inherent in academic work. In the second workshop, students critiqued discipline-specific examples of AI-generated academic assignments. By exploring the qualities of academic writing alongside GenAI outputs, the workshop series invited students to explore the possibilities of what might be achieved through human-AI collaboration and to articulate what can never be replicated by a tool without embodied knowledge. This paper presented this set of workshops as a possible model for discussing GenAI tools with students—a model that demonstrates how GenAI tools might be integrated into students’ academic practices in ways that are ethical and effortful and which support, rather than stifle, student creativity.

The Role of Artificial Intelligence in Higher Education

The integration of Artificial Intelligence (AI) in higher education has sparked a transformative shift in pedagogical methodologies, student engagement, and academic integrity across the UK and Ireland (Dogru, et.al, 2023). This qualitative study delves into the multifaceted implications of utilising AI in academic assignments from the perspective of higher education students, drawing on semi-structured interviews carried out with higher education students. The findings reveal a variety of perceived benefits, including enhanced study efficiency, personalised learning support (Hanaba et al., 2020; Yang, 2021), and the potential of AI to level the academic playing field for students with diverse needs. However, these advantages are in contrast with students’ perceptions of a wide range of significant concerns and challenges, notably the ethical dilemmas surrounding academic honesty, the potential for student dependency on AI leading to diminished knowledge and skill development, as well as the issue of equity in relation to students’ access to AI resources. Moreover, the study stresses a critical gap in institutional guidance regarding AI use, with students voicing a need for clear, consistent guidance from universities. This paper highlights the complex landscape of AI in academia, advocating for a balanced approach that harnesses AI's potential while addressing ethical, educational, and equity challenges. The study emphasises a need for immediate and more in-depth research into the use of AI in higher education from the perspective of both students and university staff in Ireland/UK.

“Should you really chat while reading?” effects of on-screen multitasking and text disfluency on integrated understanding

Students' multitasking during lectures or academic assignments is a common activity that may have negative effects on performance. It is therefore relevant to investigate under what conditions such negative effects can be reduced. The current study brought together two separate lines of research to examine the effects of on-screen multitasking and text disfluency. Multitasking required students to read and respond to social media messages during reading. Text disfluency was perceptual in the form of harder-to-read versus easy-to-read text. We included 208 university students, who were randomly assigned to one of the four conditions resulting from a 2 x 2 between-subjects design. Participants read two dual-positional texts on sun exposure and health. Dependent on the assigned condition, they could read perceptually fluent or disfluent texts, and they could intermittently receive, or not receive, on-screen social media messages while reading. As outcome variables, we measured participants’ perception of cognitive load, integrated text understanding, and metacognitive calibration of text understanding. We also controlled the possible contributions of the individual differences of prior knowledge, reading comprehension skills, working memory, and cognitive flexibility. Results showed a negative effect of multitasking on the integrated understanding of the dual positional texts with non-multitaskers outperforming multitaskers. Perception of cognitive load and metacognitive calibration were not affected by multitasking. No impact of disfluency was observed for any of the outcome variables, and the interactive effect of multitasking and disfluency was also not statistically significant. Limitations and directions for future research are discussed.

Digital Discourse on the ChatGPT Controversy: Reflections on the Controversial Use of Artificial Intelligence Among Indonesian Youth

At the end of 2022, a nonprofit technology research institution funded by Altman and Musk released an AI-based chatbot, ChatGPT, which within just three months has shown utility across various industries, particularly in jobs like copywriting, news report writing, customer service, and legal document creation. Its ability to provide coherent and insightful answers, as well as serve as a brainstorming partner, has some college professors concerned that this machine may replace various human jobs (Stokel-Walker, 2022). One likely negative impact is students using this AI-based writing tool to complete academic assignments in the form of essays (Hutson, 2022). Another implication is that researchers (both students and lecturers) may be able to compose scientific texts, partially if not wholly, and escape the radar of AI-written text detection tools (Kim, 2022), as well as peer reviewers (Else, 2023). The methodology used is digital discourse, aiming to reveal how youth discourse influences the presence of the ChatGPT application and how the consumption practice of the application impacts their creativity and critical thinking skills. The findings of this study are expected to contribute to understanding the impact of AI-based chatbots on critical thinking abilities among young people as a basis for development strategies to enhance critical and creative thinking skills in the era of Artificial Intelligence.

Understanding Underperformance in EFL Students: Factors Affecting the Comprehension of Writing Assignments

Abstract When it comes to effectively advancing the educational process by motivating students to become engaged with course material and exercise independent thought, assignments are essential. This paper focuses on how to help EFL students to read and understand correctly their academic assignments while emphasizing the importance of comprehension skills and critical thinking. The paper includes a structured approach that outlines a comprehensive framework for teaching EFL students how to read assignments effectively by integrating both theoretical insights and practical classroom applications. It also shows how correctly understanding an assignment facilitates students’ success and motivation development. The discussion sparked in this paper is supported by both theoretical insights and some practical examples.

Analisis Kendala dalam Penggunaan Website Berbasis Artificial Intelligence (AI) sebagai Alat Bantu dalam Mengerjakan Tugas Akademik pada Mahasiswa Pendidikan Agama Islam Universitas Negeri Padang

This study explores the challenges faced by Islamic Education students at Universitas Negeri Padang in using AI-based websites to complete academic assignments. Using a qualitative method with a case study approach, data was collected through interviews, observations, and documentation, then analyzed to ensure validity. The findings indicate that websites such as ChatGPT, Perplexity, and Gemini.ai are used to answer academic questions, find scientific references, and analyze images and texts. However, challenges such as AI's limited understanding of religious studies, dependence on technology, and information reliability remain significant obstacles. Students use AI for structured assignments by obtaining topic explanations and scientific references, as well as for independent tasks such as studying lecture materials, creating presentations, translating languages, and answering discussion questions. While AI use has proven beneficial, the existing challenges need to be addressed to maximize its potential.

Self-Instruction Techniques to Reduce Academic Procrastination among Indonesian University Students

Academic procrastination is the behavior of postponing completing academic assignments until the next time and replacing them with other enjoyable activities. This research aims to examine the success of self-instruction technique interventions in reducing academic procrastination experienced by students in Central Java. This research used a group one-group test experimental design. The data collection method uses a scale based on aspects of academic procrastination proposed by Steel (2007), namely Perceived time, Intention-action gap, Emotional distress and Perce, and ability. The subjects in this research were twenty students who showed high academic procrastination behavior based on the academic procrastination scale. The collected data were then analyzed using a parametric test with the T-test technique. The analysis results showed a significance value of 0.000 (p<0.05) which means that there was a decrease in academic procrastination behavior after being given an instruction technique intervention. So it can be concluded that the self-instruction technique is effective in reducing the level of student academic procrastination.

Implementing a proposed framework for enhancing critical thinking skills in synthesizing AI-generated texts

Since the development of open and low-cost generative artificial intelligence (GenAI), the higher education community has witnessed high use of AI-generated texts in scholarly research and academic assignments, attracting ongoing debate about whether such practices constitute cheating. While scholars argue that integrating GenAI tools can enhance productivity, critics raise concerns about the negative effect of such integration on critical thinking (CrT). This study therefore proposed a framework for enhancing students’ CrT skills in synthesizing AI-generated information. The proposed framework is underpinned by various theoretical foundations, encompassing five interconnected step-wise phases (familiarizing, conceptualizing, inquiring, evaluating, and synthesizing). The study was conducted under two separate experiments. The first experiment (Study 1) validated the effectiveness of the proposed framework, providing CrT training to 179 postgraduate students. In the second study (n = 125), additional experiments were undertaken to confirm the effectiveness of the framework in different contexts. An experimental procedure involving pretest and posttest design was implemented wherein participants were randomly allocated to one of three groups: experimental group 1 (exposed to our framework), experimental group 2 (exposed to an alternative self-regulated learning framework), and a control group (exposed to a non-structured framework). Results from Study 1 revealed that the framework enhances students’ CrT skills to synthesize AI-generated texts. However, these CrT skills manifested through various rigorous training aimed at reinforcing learning. While the proposed framework holds considerable value in cultivating CrT skills, significant differences arise across various personality traits. In Study 2, the framework proved to be effective in different contexts. However, it did not make a difference, particularly in its capacity to enhance students’ self-regulated learning compared to other frameworks. We discussed the implications of the findings and recommended it to educators seeking to prepare students for the challenges of the AI-driven knowledge economy.

The Influence of Technology Literacy and the Use of Artificial Intelligence (AI) By Hasanuddin University Students on the Change of Habits in Completing Academic Tasks

With the rapid advancement of technology and the integration of artificial intelligence in various aspects of life, students' approach to academic tasks is evolving. This study aims to analyze the effect of using AI on changes in habits in completing academic assignments. The type of research used is descriptive quantitative with multiple Linear Regression method. In this study, the sample was drawn with cluster random sampling of 400 people who had used AI which was divided into 16 faculties at Hasanuddin University. The data collection method in this study uses a questionnaire distributed online via kuesio.id which contains statements, interviews and documentation by searching the internet. In testing data analysis techniques, namely validity and reliability tests calculated using the SPSS (Statistical Product and Service Solutions) program with Cronbach Alpha (α) above 0.60, so the overall statement is declared reliable. The results of this study indicate that the literacy level of Hasanuddin University students is categorized as good with a respondent achievement rate (TCR) of 78.3% and the use of AI that is often used from the 15 applications listed by researchers, namely ChatGPT by 26.7%, Perplexity 12.7%, and Grammarly 12.6%. The results of data analysis show that there is a positive correlation between technological literacy and the ability to use AI with changes in habits in completing academic tasks. Students who have high literacy and are skilled in utilizing AI tend to be more efficient and effective in completing their tasks.

English academic writing for academic purposes and beyond: the experiences of undergraduate students in Hong Kong

ABSTRACT Drawing on an academic literacies approach to understand writing as a social practice, this paper explores the experiences of how undergraduate students make sense of and engage with English academic writing conventions in relation to their writing purposes. We traced the writing process of 12 undergraduate students in Hong Kong over the course of two semesters and invited them to talk over their texts at three different stages of their writing process. Our findings reveal the existence of a language-content dichotomy in which language courses tend to value English standardisation and academic style conventions while subject courses value argumentation in the assignments. A majority of students accommodated writing conventions for the achievement of good grades. This form of purpose, however, limits student agency. Designing academic assignments without the acknowledgment of wider non-academic relevance to students’ lives only reinforces a performance-oriented culture and alienates students from agentive learning. We suggest a pedagogical space for academic writing that can extend beyond academic conventions, allowing students to reflect on its social relevance. Creating a space for this exploration would foster an inclusive and empowering learning environment that facilitates student agency in academic language development and meaningful engagement with real-life issues.

A Correlational Study of the Relationship Between ChatGPT Usage & Cognitive Flexibility

Amidst the growing integration of AI into educational practices, concerns arise regarding how unregulated interaction with AI outside the structured classroom environment may influence university students’ cognitive functions. This research examines the differential effects of ChatGPT usage intensity on university students’ cognitive flexibility. The researchers hypothesize a negative correlation between ChatGPT usage and cognitive flexibility, measurable by increased response times and decreased accuracy between the two tasks of the Arrow Switch Test. We employed this test to contrast the performance of frequent (participants who used ChatGPT ≥ 50% of the time on academic assignments) and infrequent (participants who used ChatGPT ≤ 35% of the time on academic assignments) groups. For the purpose of this study, infrequent users were used as a control group. Both groups displayed a statistically significant decrease in task accuracy—highlighting the Arrow Switch Task’s efficacy in assessing cognitive flexibility—and response time between the first and second task. Notably, infrequent users of ChatGPT demonstrated a larger decline in accuracy and response time following changed task conditions compared to frequent users. This finding calls for further investigation into the longitudinal effects of AI tools on learning processes, necessitating a larger sample size and a more granular analysis of usage patterns to understand the subtleties of AI’s impact on cognitive flexibility.

The Vision of University Students from the Educational Field in the Integration of ChatGPT

ChatGPT has significantly increased in popularity in recent months because of its capacity to generate novel content and provide genuine responses to questions. Nevertheless, like all technologies, it is crucial to assess its limitations and features prior to implementing it into an educational setting. A major obstacle associated with ChatGPT is its tendency to produce consistent yet occasionally unreliable and inaccurate responses. Our study provides students with training in this area, and its objective was to analyse the opinion of those same university students studying education-related degrees regarding the efficacy of the usefulness of ChatGPT for their learning. We used a mixed methodology and two instruments for data collection: questionnaires and discussion groups. The sample comprised 150 university students pursuing degrees in teaching and social education. The results show that the majority of students are familiar with the technology but have not had any formal training in a university. They use this tool to complete academic assignments outside the classroom, and they emphasise the need for training in it. Furthermore, following the training, the students highlight an increase in motivation and a positive impact on the development of generic skills, such as information analysis, synthesis and management, problem solving, and learning how to learn. Ultimately, this study provides an opportunity to consider the implementation of educational training of this tool at the university level in order to ensure its appropriate use.

The Role of Emotional Regulation as a Mediator of Self-Compassion and Stress in Students Completing Final Assignments

Final semester students must complete their education on time with a variety of academic assignments. Apart from that, the personal problems they experience are also increasingly complex, making final-semester students vulnerable to depression. This research aims to determine the relationship between self-compassion and stress mediated by emotional regulation in students who complete their final assignments. The design used in this research was quantitative, with three variables. Data were collected using the Self-Compassion Scale with a Cronbach Alpha reliability of 0.872, the Emotion Regulation Questionnaire (ERQ) with a Cronbach Alpha reliability of 0.790, and the stress subscale of the Depression Anxiety Stress Scale (DASS-42) with a Cronbach Alpha reliability of 0.951. The subjects of this research were final-year students who were pursuing higher education in the Special Region of Yogyakarta. Data analysis used a mediation analysis model by testing the relationship between stress (dependent variable) and self-compassion (independent variable) and emotional regulation, cognitive reappraisal facets, and expressive suppression facets (mediator variables) using mediation analysis techniques via JASP. The results showed that the cognitive reappraisal facet of emotional regulation mediated the relationship between self-compassion and stress in students. The emotional regulation facet of expressive suppression did not mediate the relationship between self-compassion and stress in students. In addition, there was a direct negative relationship between self-compassion and stress. Students who complete their final assignment must make efforts to increase self-compassion in order to survive stressful situations.

The Effect of Self-Regulated Learning on Student Academic Procrastination

Background: This also happens to PGSD students. Based on observations, show that the majority of students have carried out academic procrastination such as delaying doing coursework, delaying studying when facing exams, and sometimes choosing to do things that are more fun and unrelated to academic assignments. The emergence of academic procrastination behavior is caused by several factors, including low intrinsic motivation, laziness in studying, lack of discipline in the way parents educate them, influence from friends in their environment, as well as a lack of self-regulated learning (SRL) in managing time for doing assignments with other things. Aim: This study aims to determine the effect of self-regulated learning on academic procrastination of Muhammadiyah University students of Palangkaraya. Method: This type of research is quasi-experimental research. The population of this study was 2714, while the sample was 349 students. Data were collected by using several instruments in the form of an academic procrastination scale and a self-regulated learning scale. Statistical testing using linear regression analysis. Results and Dıscussion: The results of the study indicated that the significance value is 0.025 <0.05. Conclusion: It can be concluded that there is a significant influence on the self-regulated learning on academic procrastination of Muhammadiyah University students of Palangkaraya.

Zillennials, Social Media and Artificial Intelligence: A Survey on West Java’s Digital Natives

Today’s rapid digital and artificial intelligence development has a significant impact on the lives of generation Z, also known as Zillennials. This study is part of the research initiative taken by the Indonesian Association of Communications Scholars of West Java Region (ISKI Jabar) , and aims to identify and map trends in the usage of social media and artificial intelligence among the region's Zillennials. The survey was conducted on 515 respondents aged 12-27 years, covering aspects of device utilization, intensity of internet and social media use, and perceptions of AI. Based on the study's findings, West Java Zillennials had a high level of device ownership and internet usage intensity, reaching 93% with an average daily duration of 5-6 hours. Instagram, TikTok, and Twitter have emerged as the primary social media platforms for Generation Z, substituting Facebook's previous supremacy. The integration of AI, particularly Google Assistant and ChatGPT, is increasingly becoming prevalent in everyday life, mostly employed to assist with work and academic assignments, as well as for information retrieval. Although most Zillennials display excitement towards the utilization of AI, their technical comprehension of AI classifications and functionalities remains restricted. The findings have significant implications for understanding the influence of technology on the lives of Zillennials. They also emphasize the importance of enhancing media literacy skills to properly embrace the digital and artificial intelligence era.

Teaching note: personal attack or the personal touch? Evaluating the use of video feedback methods with qualifying social workers

ABSTRACT Social work skills such as the ability to reflect on self and to recognize and manage emotions are fostered in part through using and receiving feedback, however there is limited research about feedback methods that effectively enable social work students and post-qualifying workers to engage with the emotional aspects of learning and feedback. This teaching note reports on a pilot project in which video feedback on academic assignments was provided to first year undergraduate social work students studying at a university in England. The project findings underscore the importance of engaging with the emotional dimensions of feedback processes and recognizing how feedback experiences can shape emerging learner and professional identities. The findings also underline the psychological and social skills required to engage in more performative aspects of contemporary education and practice. We argue that social work students are likely to benefit from support to develop meta-cognition (the process of thinking about one’s thinking processes) and self-regulation (regulation of emotion and behavior) skills before they can make sense of academic and practice-orientated feedback. Video feedback on academic assignments is therefore best utilized when it is underpinned by a dialogical approach to teaching and learning in the ‘classroom’ and the ‘field’.

Detecting contract cheating through linguistic fingerprint

Contract cheating, the act of students enlisting others to complete academic assignments on their behalf, poses a significant challenge in academic settings, undermining the integrity of education and assessment. It involves submitting work that is falsely represented as the student’s own, thus violating academic standards and ethics. The advent of artificial intelligence-based language models, such as ChatGPT, has raised concerns about the potential impact of contract cheating. As these language models can generate human-like text with ease, there are concerns about their role in facilitating and increasing contract cheating incidents. Innovative approaches are thus needed to detect contract cheating and address its implications for academic integrity. This study introduces a machine learning (ML) model focused on identifying deviations from a learner’s unique writing style (or their linguistic fingerprint) to detect contract cheating, complementing traditional plagiarism detection methods. The study involved 150 learners majoring in engineering and business who were studying English as a foreign language at a college in Saudi Arabia. The participants were asked to produce descriptive essays in English within a consistent genre over one semester. The proposed approach involved data preprocessing, followed by transformation using Term Frequency-Inverse Document Frequency (TF-IDF). To address data imbalance, random oversampling was applied, and logistic regression (LR) was trained with optimal hyperparameters obtained through grid search. Performance evaluation was conducted using various metrics. The results showed that the ML model was effective in identifying non-consistent essays with improved accuracy after implementing random oversampling. The LR model achieved an accuracy of 98.03%, precision of 98.52%, recall of 98.03%, and F1-score of 98.24%. The proposed ML model shows promise as an indicator of contract cheating incidents, providing an additional tool for educators and institutions to uphold academic integrity. However, it is essential to interpret the model results cautiously, as they do not constitute unequivocal evidence of cheating but rather serve as grounds for further investigation. We also emphasize the ethical implications of such approaches and suggest avenues for future research to explore the model’s applicability among first-language writers and to conduct longitudinal studies on second-language learners’ language development over longer periods.

The use of AI Chatbots in higher education: the problem of plagiarism

Background: The use of ChatGPT in the learning process is becoming a common practice. Researchers identify opportunities to improve the learning process using AI tools. At the same time, there are many unresolved problems and threats from the use of ChatGPT. These include unreliable information, false information, lack of references to primary sources, lack of intellectual property protection, and especially the problem of plagiarism in academic texts. Objectives: The purpose of the study is to summarise the results of published research on the benefits and threats of using ChatGPT in higher education and to analyse the experience of using AI to write academic assignments by university students in compliance with the requirements of academic integrity. Methods: A survey was conducted among Kyiv National Economic University named after Vadym Hetman (KNEU) students about their experience of using ChatGPT in performing academic tasks and the degree of satisfaction with this tool. Results: The survey involved 58 KNEU students. We have analysed how satisfied students are with using ChatGPT for different learning purposes. Students are most satisfied with using ChatGPT to quickly find information and translate texts. The majority of respondents said that ChatGPT does not always provide accurate and reliable information. Students also pointed to the problem of violating academic integrity when using ChatGPT to complete their assignments. Conclusions: The study shows the general advantages and disadvantages of using ChatGPT in higher education. Particular attention should be paid to the level of borrowing in academic texts prepared with the help of ChatGPT.

Ask ChatGPT first! Transforming learning experiences in the age of artificial intelligence

ABSTRACT Chatbots have transformed educational practices, and ChatGPT represents a significant development in this domain. However, more research is needed to determine how user perceptions influence the overall user experience with ChatGPT. To address this gap, this study investigates the role of ChatGPT, a revolutionary artificial intelligence – (AI)-powered chatbot, in educational settings by investigating its impact on user experience from the perspective of students. It focuses on key perceptions, including human-like personality, human-like empathy, and ChatGPT efforts, and their influence on the user experience. It also explores how ChatGPT’s efforts and usage depth affect user credibility, which in turn shapes the overall user experience. Data from 516 students who have used ChatGPT for academic assignments are collected through a questionnaire, and seemingly unrelated regression (SUR) is employed for analysis. The findings contribute to the literature on AI chatbots and user experience in three ways: by offering empirical insights into ChatGPT’s role in education, by identifying key perceptions and their impact on user experience, and by capturing multiple dimensions of user experience for the design and implementation of ChatGPT as an educational tool. By shedding light on the interplay between user perceptions and user experience, this study provides valuable guidance for educators and developers aiming to optimize the use of ChatGPT in educational contexts and to ensure its effectiveness as a trustworthy and supportive learning tool

University Students’ Perspective on ChatGPT and Technology Literacies

In recent years, technologies utilising artificial intelligence have gained popularity. It is common for college students to use ChatGPT when writing academic assignments. Considering the importance of technology literacy in adopting ChatGPT in academic writing, this study explored how Chatbot was used by college students in Bhutan in relation to Privacy literacy, Data literacy, Information literacy, and Technology adoption (Chatbot). Using a cross-sectional survey, the data for this study was collected using an online survey and 290 students responded. Analyses were mainly quantitative. Both descriptive and inferential statistics were reported. The findings of Mann-Whitney U test suggests that male students used more technology compared to their female counterparts and statistically significant differences were found on technological adoption between the genders. However, the results of Kruskal-Wallis H test revealed that there was no statistically significant difference across four age levels. Furthermore, results of path analyses showed significant direct relationship from Data literacy to Technology adoption (Chatbot). However, no empirical support was found for Information literacy and Privacy literacy. Taken together, the findings of this study have significance to the students, lecturers, and policy planners of the Royal University of Bhutan.