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ATS1297: Academic writing

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This unit introduces students to academic writing and the attitudes, values and practices of academic culture. The unit will help students to write effective essays at a tertiary level. There is a particular focus on how academic writing presents clear reasoning with evidence to support a position, and how the academic essay differs from other types of non-fiction writing, such as the opinion piece. Students will learn key principles of grammar and academic style conventions, as well as how to use sources correctly (finding, referencing, quoting, and paraphrasing) and avoid problems of plagiarism. Students will also learn how to plan, research, edit, and proofread essays to a high standard .

1 - Analytical exercise - 25% 2 - Analytical exercise - 25% 3 - Online test - 10% 4 - Essay - 40%

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Scheduled activities may include a combination of teacher directed learning, peer directed learning and online engagement.

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Towards automatic boundary detection for human-AI collaborative hybrid essay in education

• Department of Human Centred Computing

Research output : Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

The recent large language models (LLMs), e.g., ChatGPT, have been able to generate human-like and fluent responses when provided with specific instructions. While admitting the convenience brought by technological advancement, educators also have concerns that students might leverage LLMs to complete their writing assignments and pass them off as their original work. Although many AI content detection studies have been conducted as a result of such concerns, most of these prior studies modeled AI content detection as a classification problem, assuming that a text is either entirely humanwritten or entirely AI-generated. In this study, we investigated AI content detection in a rarely explored yet realistic setting where the text to be detected is collaboratively written by human and generative LLMs (termed as hybrid text for simplicity). We first formalized the detection task as identifying the transition points between human-written content and AI-generated content from a given hybrid text (boundary detection). We constructed a hybrid essay dataset by partially and randomly removing sentences from the original studentwritten essays and then instructing ChatGPT to fill in for the incomplete essays. Then we proposed a two-step detection approach where we (1) separated AI-generated content from human-written content during the encoder training process; and (2) calculated the distances between every two adjacent prototypes (a prototype is the mean of a set of consecutive sentences from the hybrid text in the embedding space) and assumed that the boundaries exist between the two adjacent prototypes that have the furthest distance from each other. Through extensive experiments, we observed the following main findings: (1) the proposed approach consistently outperformed the baseline methods across different experiment settings; (2) the encoder training process (i.e., step 1 of the above two-step approach) can significantly boost the performance of the proposed approach; (3) when detecting boundaries for single-boundary hybrid essays, the proposed approach could be enhanced by adopting a relatively large prototype size (i.e., the number of sentences needed to calculate a prototype), leading to a 22% improvement (against the best baseline method) in the In-Domain evaluation and an 18% improvement in the Out-of-Domain evaluation.

Publication series

Access to Document

• 10.1609/aaai.v38i20.30258
• 586816684-oa Final published version, 1.41 MB

Other files and links

• Link to publication in Scopus

T1 - Towards automatic boundary detection for human-AI collaborative hybrid essay in education

AU - Zeng, Zijie

AU - Sha, Lele

AU - Li, Yuheng

AU - Yang, Kaixun

AU - Gašević, Dragan

AU - Chen, Guanliang

N1 - Conference code: 38th

N2 - The recent large language models (LLMs), e.g., ChatGPT, have been able to generate human-like and fluent responses when provided with specific instructions. While admitting the convenience brought by technological advancement, educators also have concerns that students might leverage LLMs to complete their writing assignments and pass them off as their original work. Although many AI content detection studies have been conducted as a result of such concerns, most of these prior studies modeled AI content detection as a classification problem, assuming that a text is either entirely humanwritten or entirely AI-generated. In this study, we investigated AI content detection in a rarely explored yet realistic setting where the text to be detected is collaboratively written by human and generative LLMs (termed as hybrid text for simplicity). We first formalized the detection task as identifying the transition points between human-written content and AI-generated content from a given hybrid text (boundary detection). We constructed a hybrid essay dataset by partially and randomly removing sentences from the original studentwritten essays and then instructing ChatGPT to fill in for the incomplete essays. Then we proposed a two-step detection approach where we (1) separated AI-generated content from human-written content during the encoder training process; and (2) calculated the distances between every two adjacent prototypes (a prototype is the mean of a set of consecutive sentences from the hybrid text in the embedding space) and assumed that the boundaries exist between the two adjacent prototypes that have the furthest distance from each other. Through extensive experiments, we observed the following main findings: (1) the proposed approach consistently outperformed the baseline methods across different experiment settings; (2) the encoder training process (i.e., step 1 of the above two-step approach) can significantly boost the performance of the proposed approach; (3) when detecting boundaries for single-boundary hybrid essays, the proposed approach could be enhanced by adopting a relatively large prototype size (i.e., the number of sentences needed to calculate a prototype), leading to a 22% improvement (against the best baseline method) in the In-Domain evaluation and an 18% improvement in the Out-of-Domain evaluation.

AB - The recent large language models (LLMs), e.g., ChatGPT, have been able to generate human-like and fluent responses when provided with specific instructions. While admitting the convenience brought by technological advancement, educators also have concerns that students might leverage LLMs to complete their writing assignments and pass them off as their original work. Although many AI content detection studies have been conducted as a result of such concerns, most of these prior studies modeled AI content detection as a classification problem, assuming that a text is either entirely humanwritten or entirely AI-generated. In this study, we investigated AI content detection in a rarely explored yet realistic setting where the text to be detected is collaboratively written by human and generative LLMs (termed as hybrid text for simplicity). We first formalized the detection task as identifying the transition points between human-written content and AI-generated content from a given hybrid text (boundary detection). We constructed a hybrid essay dataset by partially and randomly removing sentences from the original studentwritten essays and then instructing ChatGPT to fill in for the incomplete essays. Then we proposed a two-step detection approach where we (1) separated AI-generated content from human-written content during the encoder training process; and (2) calculated the distances between every two adjacent prototypes (a prototype is the mean of a set of consecutive sentences from the hybrid text in the embedding space) and assumed that the boundaries exist between the two adjacent prototypes that have the furthest distance from each other. Through extensive experiments, we observed the following main findings: (1) the proposed approach consistently outperformed the baseline methods across different experiment settings; (2) the encoder training process (i.e., step 1 of the above two-step approach) can significantly boost the performance of the proposed approach; (3) when detecting boundaries for single-boundary hybrid essays, the proposed approach could be enhanced by adopting a relatively large prototype size (i.e., the number of sentences needed to calculate a prototype), leading to a 22% improvement (against the best baseline method) in the In-Domain evaluation and an 18% improvement in the Out-of-Domain evaluation.

KW - General

UR - http://www.scopus.com/inward/record.url?scp=85188603524&partnerID=8YFLogxK

U2 - 10.1609/aaai.v38i20.30258

DO - 10.1609/aaai.v38i20.30258

M3 - Conference Paper

AN - SCOPUS:85188603524

T3 - Proceedings of the AAAI Conference on Artificial Intelligence

BT - Thirty-Eighth AAAI Conference on Artificial Intelligence

A2 - Wooldridge, Michael

A2 - Dy, Jennifer

A2 - Natarajan, Sriraam

PB - Association for the Advancement of Artificial Intelligence (AAAI)

CY - Washington DC USA

T2 - AAAI Conference on Artificial Intelligence 2024

Y2 - 20 February 2024 through 27 February 2024

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June 18, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

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trusted source

Rethinking English essay scores

by Kobe University

To get high scores on essay writing tests, learners of English as a foreign language need to focus on good arguments more than on complex grammar. The Kobe University finding challenges conventional approaches to test preparation and scoring rubrics.

Writing essays is a well-established tool for monitoring progress in learning English as a foreign language , as it provides a snapshot of a student's mastery of grammar and vocabulary. Especially in Japan, where English language tests are often required for university admission and students closely follow advice on how to achieve high scores on these tests, a "good essay " is often seen as one that demonstrates a high level of grammatical complexity. But is this actually reflected in test scores ?

Kobe University linguist Yasuda Sachiko says, "Based on my experience of teaching academic writing to students at various levels in Japan, I believe that linguistically complex texts do not always result in better writing."

She therefore decided to conduct an experiment with over 100 Japanese high school students. Yasuda had them write a short essay on a given topic and looked at the relationship between the linguistic complexity of the texts and the writers' ability to present complex arguments, and how these two related to how the texts were graded according to official rubrics.

She adds, "This study is the first to focus on the relationship between features of linguistic complexity and features of meaning complexity; no one else in the relevant fields has looked at the relationship between these two."

The results, published in the journal Assessing Writing , confirmed her suspicions. She found that high-scoring essays shared features related more to the ability to express complex meaning, such as lexical diversity, noun modification, and soundness and number of arguments, than to structural complexity.

"Interestingly, low scoring essays showed the highest level of complexity in finite adverbial dependent clauses," the linguist writes in her paper. Emphasizing this point, the ability to express complex meaning was strongly correlated only with using diverse expressions and the ability to modify their meaning, but not with grammatical features.

Yasuda concludes, "Simply having complex sentence structures does not necessarily lead to a better essay."

The findings have implications for how essay writing tests are scored. The Kobe University researcher explains, "Current rubrics for writing questions on language tests instruct test-takers to 'use complex grammar appropriately' or 'a variety of complex structures."

However, since sentence complexity does not significantly affect overall essay quality, it may be more appropriate to use terms such as 'contextually appropriate grammar' or 'genre-appropriate grammar.'

Thus arguing that the ability to express one's opinion in varied and complex ways is a marker of students' writing ability, she advocates that this characteristic should be more represented both in the way tests are scored and how feedback is provided to students.

This so-called washback effect of test scoring rubrics on the way language is taught is at the heart of what drives Yasuda.

She says, "I am committed to using the results of this study for practical applications, such as refining assessment criteria for evaluating students' writing, developing tasks and materials to improve their writing skills, and identifying the key knowledge that teachers need to help students become better writers.

"The ability to write in English has become increasingly important in the 21st century, as it is a crucial medium that allows us to connect with others around the world."

Provided by Kobe University

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