The DOI matches its Crossref record; 50 of the 52 references it cites were found in a registry or an index; none of the 74 sources its claims cite is checked for support yet.
Generative artificial intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications
Open accessAbstract
This paper explores recent advancements and implications of artificial intelligence (AI) technology, with a specific focus on Large Language Models (LLMs) like ChatGPT 3.5, within the realm of higher education. Through a review of the academic literature, this paper highlights the unprecedented growth of these models and their wide-reaching impact across various sectors. The discussion sheds light on the complex issues and potential benefits presented by LLMs, providing a overview of the field's current state. In the context of higher education, the paper explores the challenges and opportunities posed by LLMs. These include issues related to educational assessment, potential threats to academic integrity, privacy concerns, the propagation of misinformation, EDI aspects, copyright concerns and inherent biases within the models. While these challenges are multifaceted and significant, the paper emphasizes the availability of strategies to address them effectively and facilitate the successful adoption of LLMs in educational settings. Furthermore, the paper recognises the potential opportunities to transform higher education. It emphasises the need to update assessment policies, develop guidelines for staff and students, scaffold AI skills development, and find ways to leverage technology in the classroom. By proactively pursuing these steps, higher education institutions (HEIs) can harness the full potential of LLMs while managing their adoption responsibly. In conclusion, the paper urges HEIs to allocate resources to handle the adoption of LLMs effectively. This includes ensuring staff AI readiness and taking steps to modify their study programmes to align with the evolving educational landscape influenced by emerging technologies.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This review synthesizes literature on generative AI and large language models, especially ChatGPT 3.5, in higher education, outlining challenges around academic integrity, assessment, copyright, EDI, privacy, and misinformation alongside opportunities for AI literacy and transformed teaching. It argues that higher education institutions should update assessment and misconduct policies, support staff AI readiness, and allocate resources to adopt LLMs responsibly.
Key findings
- ChatGPT’s adoption was unprecedented: it reached an estimated 100 million monthly active users two months after launch, and a January 2023 survey of over 1,000 university students found more than one-third used ChatGPT for assessments, with 75% of those believing it was cheating but still using it.
- Academic integrity is threatened because AI-generated text is often undetectable; detection tools perform only marginally better than a random classifier, are prone to false positives and false negatives, and can be biased against non-native English writers.
- Copyright, privacy, and misinformation concerns are prominent: GPT-2 was trained on 40 GB of text and GPT-3 on 570 GB, while GPT-4’s dataset size is undisclosed; lawsuits have been filed against OpenAI and Google; Italy restricted ChatGPT in 2023; and LLMs can hallucinate or fabricate references.
- Opportunities include using chatbots to support disadvantaged students, non-native English speakers, and neurodivergent students; embedding AI literacy, prompt engineering, and generative AI use cases into curricula; and providing personalised learning and immediate feedback.
Methods
- Comprehensive narrative review of academic literature focused on LLMs such as ChatGPT 3.5 in higher education.
- Synthesizes sources across themes including academic integrity, assessment design, available solutions, copyright, EDI, information privacy, misinformation, and opportunities.
- No primary data collection or systematic search protocol is reported; the author used ChatGPT 3.5 and Claude 2 for proofreading and ChatGPT 3.5 API with AgentGPT to search for relevant publications, news stories, and themes.
Limitations
- The paper does not state explicit limitations.
- Evident from the text: it is a narrative review without a systematic search or inclusion criteria, relying on secondary literature, news, and preprints in a fast-moving technological field.
- Evident from the text: it notes limited actual evidence of mass-scale cheating at the time of writing, and the author used generative AI tools in manuscript preparation.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
72 Not checked yet · 27 Own interpretation · 11 No basis given
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While ChatGPT can undeniably be used for academic cheating, at the point of writing, there is limited actual evidence of a mass-scale cheating taking place.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Threat to academic integrity
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
In the paper
“Increasing reliance on this tool amplifies concerns about academic honesty and plagiarism risk (Stokel-Walker, 2022).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Threat to academic integrity
What the sources say
- Not checked yetcites Stokel-Walker, 2022
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“The same study finds that the detection tool is also bypassed by changing prompting strategy.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Threat to academic integrity
What the sources say
- Not checked yetcites Liang et al., 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“leading to a performance that is only marginally better than a random classifier (Sadasivan et al., 2023)”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Threat to academic integrity
What the sources say
- Not checked yetcites Sadasivan et al., 2023
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“As ChatGPT is trained using a large amount of text data, such as books, articles, and other written materials, some of the training data may be copyrighted (Karim, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Copyright concerns
What the sources say
- Not checked yetcites Karim, 2023
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Prominent topics were essay writing, coding, and evasion techniques.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Threat to academic integrity
What the sources say
- Not checked yetcites Haensch et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Moreover, the existing AI detection tools are prone to false positives and false negatives due to variable human writing styles (Dalalah and Dalalah, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Threat to academic integrity
What the sources say
- Not checked yetcites Dalalah and Dalalah, 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Even if some potentially feasible solutions such as credible AI detection tools and watermarking of AI-generated output, students may also opt to edit the AI-generated outputs or use other tools to make the results less identifiable as machine-generated (Lancaster, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Threat to academic integrity
What the sources say
- Not checked yetcites Lancaster, 2023
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Finally, there are reports of bias against non-native English writers due to the design of the detection tool which looks for low perplexity writing as a marker for AI-generated text, and it penalises non-native speakers with limited linguistic impressions (Liang et al., 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Threat to academic integrity
What the sources say
- Not checked yetcites Liang et al., 2023
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“A survey in January 2023 with over a thousand university students revealed that more than one-third were using ChatGPT for their assessments.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Threat to academic integrity
What the sources say
- Not checked yetcites Intelligent, 2023
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 49
- Probably not a substantial claim. 50
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the context of higher education, the paper explores the challenges and opportunities posed by LLMs.These include issues related to educational assessment, potential threats to academic integrity, privacy concerns, the propagation of misinformation, Equity, Diversity, and Inclusion (EDI) aspects, copyright concerns and inherent biases within the models.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The academic literature reviewed provides a comprehensive and representative account of the current state of generative AI in higher education.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While these challenges are multifaceted and significant, the paper emphasises the availability of strategies to address them effectively and facilitate the successful adoption of LLMs in educational settings.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Abstract
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Rests on an assumption: The academic literature reviewed provides a comprehensive and representative account of the current state of generative AI in higher education.
In the paper
“It has been reported that technology companies are in talks with leading media organisations about using their news content for AI training (Criddle et al., 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Copyright concerns
What the sources say
- Not checked yetcites Criddle et al., 2023
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The app has become the fastest-growing online application in history, reaching an estimated 100 million monthly active users in just two months after its launch (Reuters, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Introduction
What the sources say
- Not checked yetcites Reuters, 2023
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Furthermore, ChatGPT can be fine-tuned for specific tasks or domains, increasing its versatility across various industries and applications (Ray, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Introduction
What the sources say
- Not checked yetcites Ray, 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The size of the data set used for OpenAI's most recent model, GPT-4, has not been disclosed (Heikkilä, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Copyright concerns
What the sources say
- Not checked yetcites Heikkilä, 2023
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Finally, Bloomberg, the information provider for financial services that is also used by over 300 universities worldwide (Bloomberg, 2023a), is developing a new generative AI model, trained on an extensive variety of financial data owed by the company (Bloomberg, 2023b).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Copyright concerns
What the sources say
- Not checked yetcites Bloomberg, 2023b
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Further, it is important that copyrights differ between jurisdictions.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Copyright concerns
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the realm of AI development, the prevailing belief is that the larger the training data set, the better the results.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Copyright concerns
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Traditional plagiarism-detection tools are rendered ineffective in this context, as AI is becoming increasingly proficient at imitating human-like text and AI-generated text is deemed original and thus undetectable (Dwivedi et al., 2023.)”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Threat to academic integrity
What the sources say
- Not checked yetcites Dwivedi et al., 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Gonsalves (2023) suggests that educators using multiple choice test for assessment may consider outsmarting the system by taking advantage of the current limitations of generative AI platforms, such as their inability to interpret visual media or understand up-to-date information beyond its training data.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Rethinking assessment design
What the sources say
- Not checked yetcites Gonsalves (2023)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“Given TikTok's 3.7 million active UK users, with 26% aged 18-24 (Bendix, 2023), the potential for covert cheating is significant.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Threat to academic integrity
What the sources say
- Not checked yetcites Bendix, 2023
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“ChatGPT is capable of generating human-like responses using natural language, essentially leading to an expert system that is available on demand.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Introduction
What the sources say
- Not checked yetcites Haleem et al., 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“OpenAI's GPT-2 model was trained using a data set that comprised 40 gigabytes of text.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Copyright concerns
What the sources say
- Not checked yetcites Heikkilä, 2023
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It can understand the context of a conversation and generate relevant responses, making it more effective at mimicking human-like interactions.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“While chatbots are not a new invention, ChatGPT has brought several advancements to generative AI, including improved contextual understanding, language generation, task adaptability, and multilingual proficiency.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“For instance, it has been reported that two authors have filed a lawsuit against OpenAI, alleging that their copyrighted books were used to train the AI model, ChatGPT, without their permission.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Copyright concerns
What the sources say
- Not checked yetcites Creamer, 2023
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“GPT-3, the model upon which ChatGPT is built, utilised a significantly larger data set of 570 GB.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Copyright concerns
What the sources say
- Not checked yetcites Heikkilä, 2023
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For instance, Adobe's 'Firefly', a generative AI image tool, guarantees commercial usage safety, offering IP indemnification as it is trained on company-owned images (Gold, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Copyright concerns
What the sources say
- Not checked yetcites Gold, 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Trained on extensive data, it is competent across disciplines.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Threat to academic integrity
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, the paper recognises the potential opportunities to transform higher education.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Rests on an assumption: The academic literature reviewed provides a comprehensive and representative account of the current state of generative AI in higher education.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Its advanced language generation capabilities allow it to produce coherent, contextually accurate, and grammatically correct text.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“However, designing such tests may be more time consuming, difficult to do in certain subjects, and as technology improves, attempts to out-design chatbots might prove fruitless in the long term (Mills, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Rethinking assessment design
What the sources say
- Not checked yetcites Mills, 2023
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“In a separate case, Google, along with its parent company Alphabet and AI subsidiary DeepMind, has been accused in a lawsuit of scraping user data without consent and violating copyright laws to train its AI products.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Copyright concerns
What the sources say
- Not checked yetcites Dixit, 2023
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“This model uses its training data to produce an answer (Haleem et al., 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Introduction
What the sources say
- Not checked yetcites Haleem et al., 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Moreover, both Tlili et al. (2023) and Haensch et al. (2023) emphasise very little discourse related to negative aspects of using generative AI in education, meaning that systems such as ChatGPT may be used without much reflection and evaluation.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Threat to academic integrity
What the sources say
- Not checked yetcites Tlili et al. (2023)
- Not checked yetcites Haensch et al. (2023)
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Therefore, using generative AI leads to substantial risks of accidental plagiarism and copyright infringements (Gimpel et al., 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Copyright concerns
What the sources say
- Not checked yetcites Gimpel et al., 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“For instance, while ChatGPT 3.5 does not have internet access, OpenAI allows its newer model GPT 4.0 internet access through the Bing search engine or third-party plug-ins (Wiggers, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Rethinking assessment design
What the sources say
- Not checked yetcites Wiggers, 2023
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Google has updated privacy terms which now clearly state their right to use publicly available data for AI model training.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Copyright concerns
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, ChatGPT's content generation capabilities excel in these aspects, potentially creating a false sense of reliability.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Misinformation
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, embedding AI literacy in graduate skills can enhance their employability in a rapidly evolving job market.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Opportunities
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Developing use cases for generative AI is a significant challenge for educational providers.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Opportunities
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Non-native English speakers can use ChatGPT for grammar feedback and as a semi-translator for complex terms, aiding comprehension (Sullivan et al., 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Equality, Diversity and Inclusion (EDI)
What the sources say
- Not checked yetcites Sullivan et al., 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, generative AI may support research, writing, and data analysis which effectively opens new possibilities for educators and students, potentially unleashing their digital creativity.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Opportunities
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Tlili et al. (2023) examined OpenAI's policies and found that while conversations with ChatGPT are stored and used to improve its performance, the specifics of storage and use are not entirely clear.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Information privacy
What the sources say
- Not checked yetcites Tlili et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“ChatGPT has been widely criticised for producing biased and discriminatory content, which has been caused by using training data that reflects the biases of society (Weinberger, 2019;Dwivedi et al., 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Equality, Diversity and Inclusion (EDI)
What the sources say
- Not checked yetcites Weinberger, 2019
- Not checked yetcites Dwivedi et al., 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This technology may assist in creation of inventive assessments which could promote creativity and critical thinking skills, contributing to comprehensive and meaningful evaluation of learning outcomes (Rasul et al., 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Opportunities
What the sources say
- Not checked yetcites Rasul et al., 2023
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In addition, it also offers immediate feedback and advice, assisting students in rectifying mistakes and enhancing their study techniques (Javaid at al., 2023;Yu and Guo, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Opportunities
What the sources say
- Not checked yetcites Javaid at al., 2023
- Not checked yetcites Yu and Guo, 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Given the vast data processed by ChatGPT's machine learning algorithms, it is vulnerable to cyberattacks, risking unauthorised access or misuse of sensitive information (Dwivedi et al., 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Information privacy
What the sources say
- Not checked yetcites Dwivedi et al., 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“AI can also support adaptive writing and highlight essential information in various formats (Kasneci et al., 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Equality, Diversity and Inclusion (EDI)
What the sources say
- Not checked yetcites Kasneci et al., 2023
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“LLMs possess notable capabilities for education, but these are yet to be fully explored.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Opportunities
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In summary, while privacy issues are genuine, they should not hinder AI's integration into higher education.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Information privacy
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“The US Federal Trade Commission is also scrutinising OpenAI for potential false or harmful statements about real individuals and its data privacy methods (BBC News, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Information privacy
What the sources say
- Not checked yetcites BBC News, 2023
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Young learners could unintentionally share personal details with ChatGPT, underscoring the need to protect the privacy of users, particularly the younger demographic.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Information privacy
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The widespread integration of LLMs in higher education can offer substantial advantages for students and faculty.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Opportunities
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“While users should verify AIprovided information, many still fall victim to misinformation (Lim et al., 2023), due to cognitive biases, reliance on headlines, and the persistence of false information (Centre for Information Technology and Society, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Misinformation
What the sources say
- Not checked yetcites Lim et al., 2023
- Not checked yetcites Centre for Information Technology and Society, 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Moreover, the fact that AI may produce biased responses represents a valuable opportunity to raise awareness and engage in a discussion about inherent biases which are present in society and therefore reflected in the LLM's training data (Heaven, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Equality, Diversity and Inclusion (EDI)
What the sources say
- Not checked yetcites Heaven, 2023
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While AI poses challenges, it also offers solutions to bolster equality, diversity, and inclusion.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Equality, Diversity and Inclusion (EDI)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“They can handle course queries, direct students to resources, provide materials suited to different learning styles (Gupta and Chen, 2022), and simplify complex ideas, benefiting those with communication challenges (Hemsley et al., 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Equality, Diversity and Inclusion (EDI)
What the sources say
- Not checked yetcites Gupta and Chen, 2022
- Not checked yetcites Hemsley et al., 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In conclusion, while the ability of AI to bolster EDI is clear, the risks associated with AI use can be managed.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Equality, Diversity and Inclusion (EDI)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As tools like ChatGPT become more common at work, graduates must be equipped with the right knowledge and skills to use them adeptly.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Opportunities
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“Gilson et al. (2023), note that ChatGPT's initial answer could prompt further questioning and encourage students to apply their knowledge and reasoning skills”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Opportunities
What the sources say
- Not checked yetcites Gilson et al. (2023)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Once students have the necessary knowledge and skills, they can evaluate the outputs generated by AI, weeding out inaccuracies and leveraging the beneficial and helpful components of the responses offered by the technology.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Misinformation
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, fictional references, which are often markers of AI-generated content, can help educators pinpoint students falsely claiming AI work as their own.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Misinformation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is capable of generating computer code snippets in different programming languages, crafting tables and lists, and creating Excel formulas.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Opportunities
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Similarly, Japanese authorities have voiced privacy concerns, emphasising the need to weigh these against the benefits of generative AI (Kaur, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Information privacy
What the sources say
- Not checked yetcites Kaur, 2023
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“While these outputs may require some editing and verification, ChatGPT undeniably serves as a valuable time-saving resource (Centre for Teaching and Learning, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Opportunities
What the sources say
- Not checked yetcites Centre for Teaching and Learning, 2023
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Generative AI, when used appropriately in education, offers significant potential to enhance EDI and promote inclusive learning.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Equality, Diversity and Inclusion (EDI)
boostedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“The Covid-19 pandemic illustrated how AI can mass-produce questionable content and amplify misinformation through social media bots (Gisondi, et al., 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Misinformation
What the sources say
- Not checked yetcites Gisondi, et al., 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The privacy implications of ChatGPT are particularly relevant for learners and educators who may lack in-depth knowledge of technology and privacy.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Information privacy
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“OpenAI responded by addressing and clarifying these privacy matters ( McCallum, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Information privacy
What the sources say
- Not checked yetcites McCallum, 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“while Rudolph et al. (2023) state that generative AI could be used as an aid to improve writing and research skills.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Opportunities
What the sources say
- Not checked yetcites Rudolph et al. (2023)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For neurodivergent students, AI can assist in time management, information processing, and thought organisation (McMurtrie, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Equality, Diversity and Inclusion (EDI)
What the sources say
- Not checked yetcites McMurtrie, 2023
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Companies developing LLMs are aware of these issues and are working to address them (O'Brien, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Misinformation
What the sources say
- Not checked yetcites O'Brien, 2023
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Integrating these AI models into curricula can nurture creativity, promote critical thinking, and prepare students for a future where AI collaboration is commonplace in professional life.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Summary
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“It is crucial to understand that ChatGPT does not operate based on ethical principles, nor can it discern between right and wrong, or truth and falsehood.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Summary
What the sources say
- Not checked yetcites Sabzalieva and Valentini, 2023
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Through aiding research, boosting writing skills, and enabling innovative problem-solving, this technology is set to enhance the educational landscape”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Summary
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Their multifaceted attributes not only underscore heightened efficiency but also democratise AI, making it an accessible tool for diverse audiences.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Summary
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“OpenAI has recognised this issue and claims to have fine-tuned ChatGPT 4.0, achieving an 82% reduction in the model's tendency to produce disallowed content compared to its predecessor.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Summary
What the sources say
- Not checked yetcites OpenAI, 2023b
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Furthermore, Hartmann et al. (2023) highlight ChatGPT's pro-environmental, left-libertarian bias.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Summary
What the sources say
- Not checked yetcites Hartmann et al. (2023)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Generative AI and LLMs, with their capacity for personalised lessons and engaging with complex subjects, hold the potential to transform traditional classroom settings.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Summary
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“This tool merely gathers data from the databases and texts it processes online, thereby inheriting any cognitive biases present in that information(Sabzalieva and Valentini, 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Summary
What the sources say
- Not checked yetcites Sabzalieva and Valentini, 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Currently, generative AI is still a relatively novel technology, therefore the question of how to successfully integrate it within higher education remains unanswered.”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Summary
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Additionally, GPT-4 is 29% more aligned with company safety policies on sensitive data handling(OpenAI, 2023b).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Summary
What the sources say
- Not checked yetcites OpenAI, 2023b
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Overall, LLMs can perpetuate and amplify existing biases and unfairness in society, which can negatively impact teaching and learning processes and outcomes(Abdelghani et al., 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Summary
What the sources say
- Not checked yetcites Abdelghani et al., 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
The academic literature reviewed provides a comprehensive and representative account of the current state of generative AI in higher education.
“Through a comprehensive review of the academic literature, this paper highlights the unprecedented growth of these models and their widereaching impact across various sectors.”
“The discussion sheds light on the complex issues and potential benefits presented by LLMs, providing a comprehensive overview of the field's current state.”
The paper relies on this without support.
The publications, news stories and themes retrieved by the ChatGPT 3.5 API and AgentGPT search are sufficient and representative for drawing conclusions about generative AI in higher education.
“ChatGPT 3.5 API in combination with AgentGPT was used to search for relevant publications, news stories, and themes during the initial stage of the project.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“2022 has profoundly impacted Higher Education (HE), prompting universities to explore the potential of tools like ChatGPT in teaching and learning (Bobula, 2024).”
From Planets, cars, and personas: Toward an understanding of faculty perceptions of artificial intelligence through visual metaphors and projective techniques (2025) · cites this paper as Bobula, 2024
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Misinformation
“The Covid-19 pandemic illustrated how AI can mass-produce questionable content and amplify misinformation through social media bots (Gisondi, et al., 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > References
“Gisondi, M.A., Barber, R., Faust, J.S., Raja, A., Strehlow, M.C., Westafer, L.M. and Gottlieb, M. (2022) 'A Deadly Infodemic: Social Media and the Power of COVID-19 Misinformation'”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Introduction
“This model uses its training data to produce an answer (Haleem et al., 2023).”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > References
“Haleem, A., Javaid, M. and Singh, R.P. (2022) 'An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges'”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > Rethinking assessment design
“ensuring their voices are heard and their opinions hold significance (McMurtrie, 2022)”
§ Generative Artificial Intelligence (AI) in higher education: a comprehensive review of challenges, opportunities, and implications > References
“McMurtrie, B. (2023) 'How ChatGPT could help or hurt students with disabilities'”
Citations
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What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education
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“ChatGPT displayed competence in operations management and case analysis but struggled with basic calculations and intricate problems, potentially due to difficulty in deeply comprehending and applying concepts.”
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“However, little is known about teachers' knowledge and skills to integrate AI-based tools (Celik, 2023).”
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Cited by
Sorted by citations
Generative AI in Higher Education: Balancing Innovation and Integrity
Francis, N. J., Jones, S., & Smith, D. P. (2025) · British Journal of Biomedical Science · doi:10.3389/bjbs.2024.14048 · Cited by 295
“These developments have enabled GenAI systems to start performing complex tasks that were previously unattainable, thus opening new avenues for their application in all aspects of education [7].”
Evangelista, E. D. L. (2025) · Contemporary Educational Technology · doi:10.30935/cedtech/15775 · Cited by 124
“With this, De Maio (2024) and Bobula (2024) argue that policies must undergo regular updates to keep pace with the rapidly evolving challenges AI presents.”
Salido, A., Syarif, I., Sitepu, M. S., Suparjan, Wana, P. R., Taufika, R., & Melisa, R. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.101924 · Cited by 48
Schwarz, J. (2025) · Teaching Statistics · doi:10.1111/test.12398 · Cited by 48
“1, 11 However, the impact, or repercussions, of the use of generative AI in the field of statistics education and data analysis have been accorded only little attention in the discussion so far.”
Scaffolding AI literacy: An instructional model for academic librarianship
LaFlamme, K. A. (2025) · The Journal of Academic Librarianship · doi:10.1016/j.acalib.2025.103041 · Cited by 35
Yeung, R. S. K., Tian, R., Chiu, D. K. W., & Choi, S. P. M. (2025) · The Journal of Academic Librarianship · doi:10.1016/j.acalib.2025.103082 · Cited by 45
Géring, Z., Fehér, K., Harmat, V., & Tamássy, R. (2025) · International journal of organizational analysis · doi:10.1108/ijoa-09-2024-4850 · Cited by 46
Artificial intelligence in higher education, opportunities, and challenges: a review
AlBlooshi, S. (2026) · Frontiers in Education · doi:10.3389/feduc.2025.1683968 · Cited by 62
“AI detection is fallible due to the variety of writing genres, resulting in both false positives and negatives (Bobula, 2024; Alier et al., 2024).”
InfluentialA review on enhancing education with AI: exploring the potential of ChatGPT, Bard, and generative AI
Abebe, A. (2025) · Discover Education · doi:10.1007/s44217-025-00426-5 · Cited by 24
Navigating anxiety in academia: the role of generative artificial intelligence
Jabali, O. M., Saeedi, M., & Alawneh, Y. J. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13433-8 · Cited by 20
“The rapid pace of AI advancements makes it difficult for educators to keep up with new tools and methodologies (Bobula, 2024).”
Discovering how students use generative artificial intelligence tools for academic writing purposes
Johnston, H., Eaton, M., Henry, I., Deeley, E.-M., & Parsons, B. N. (2025) · Journal of Learning Development in Higher Education · doi:10.47408/jldhe.vi34.1301 · Cited by 12
Wang, S., Xu, L., Liu, J., & Zhai, Y. (2025) · Journal of Data Science · doi:10.6339/25-jds1208 · Cited by 11
Sok, S., & Heng, K. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4806030 · Cited by 14
Eyal, L. (2025) · Education Sciences · doi:10.3390/educsci15111452 · Cited by 20
“Furthermore, the findings highlight the need for curriculum design that integrates content knowledge and holistic AI integration into technology courses—thereby avoiding a narrow technical focus (Bobula, 2024; Ng et al., 2021).”
AI in Criminal Justice Education: Assessing Educator’s Attitudes and Experiences
Tyler, D. H. F., Gagnon, A., & George, S. S. (2024) · Journal of Criminal Justice Education · doi:10.1080/10511253.2024.2413995 · Cited by 9
“Scholars have documented educators’ concerns about plagiarism and the authenticity of students’ work (Bobula, 2024; uzun, 2023).”
Murray, M. C. (2025) · Issues in Informing Science and Information Technology · doi:10.28945/5516 · Cited by 11
“…clear guidelines that address appropriate use cases, citation and attribution practices, verification procedures, and ethical considerations (Bobula, 2024; Francis et al., 2025) Integrating generative AI into higher education presents transformative potential accompanied by many…”
Paredes-Aguirre, M., Bolaños-Mendoza, C., Ortíz-Rojas, M., Martinez-Tovar, J., Sanchez-Mendoza, M., & Moreno-Abramowicz, M. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2614093 · Cited by 12
Artificial Intelligence in Educational Technology: A Systematic Review of Datasets and Applications
Topham, L., Atherton, P., Reynolds, T., Hussain, Y., Hussain, A. J., Kolivand, H., & Khan, W. (2025) · ACM Computing Surveys · doi:10.1145/3768312 · Cited by 9
“Furthermore, such technology presents additional issues such as copyright issues, bias, unfairness, difficulty distinguishing model-generated answers from human-generated answers, training costs, data privacy, content errors, and more [3, 51].”
Generative artificial intelligence – transformations in education: prospects and dynamics
Всеволодовна, М. Т. (2025) · Man and Education · doi:10.54884/1815-7041-2025-83-2-142-153 · Cited by 8
Harrington, B., Zlotnikova, I., Nadarajan, G., & Ekundayo, S. (2025) · ACM Transactions on Computing Education · doi:10.1145/3776558 · Cited by 11
“Bobula [4] suggests that universities can harness the benefits of LLMs while mitigating risks through strategies such as updating assessment designs, providing AI literacy training, and implementing clear usage guidelines.”
Reddy, C. S., & Leelavathi, R. (2025) · International Journal of Educational Management · doi:10.1108/ijem-12-2024-0843 · Cited by 11
Tyler, D. H. F., George, S. S., Gagnon, A., & Howard, J. (2025) · Journal of Criminal Justice Education · doi:10.1080/10511253.2025.2506435 · Cited by 5
Duah, J. E., Lu, X. B., McGivern, P., & Jing, Y. (2024) · International Conference on Big Data and Education (ICBDE) · doi:10.1145/3704289.3704293 · Cited by 9
“However, despite the existence of comprehensive reviews on the use of AI within HE, such as the meta systematic review conducted by [8, 9, and 10], notable gaps persist.”
Karakitsou, C., Karayianni, I., Leftheriotis, K., Logotheti, A., & Avgerinou, M. D. (2025) · Journal of Visual Literacy · doi:10.1080/1051144x.2025.2538256 · Cited by 3
“The wider accessibility of Generative Artificial Intelligence (GenAI) since November 2022 has profoundly impacted Higher Education (HE), prompting universities to explore the potential of tools like ChatGPT in teaching and learning (Bobula, 2024).”
I'm a chatbot, ask me anything: using ChatGPT to improve learning experiences
Pickard-Jones, B., Thomson, S. M., Baines, S., & Otermans, P. C. J. (2024) · Journal of Learning Development in Higher Education · doi:10.47408/jldhe.vi32.1414 · Cited by 3
Redefining Learning in the Digital Age
Baek, E.-O., Wilson, R. V., & Liu, C. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-6130-6.ch006 · Cited by 3
Generative Artificial Intelligence and Postsecondary Education
Gierhart, A. R., Shefferly, N., Li, Y., & Speetzen, E. D. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-5633-3.ch002 · Cited by 4
Zhang, Y., Gong, X., Wang, X., Wang, Z., Zhou, J., & Wang, Y. (2025) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste64638.2025.11092216 · Cited by 3
“Through the interview analysis conducted in this study, it is evident that AIGC tools have significant positive impacts on college students' self-directed learning, such as improving learning efficiency, inspiring learning creativity, and assisting academic writing [5].”
Williams, A. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2698137 · Cited by 2
Zirak, R., Miakhel, I., & Rokhan, R. (2025) · Journal of Social Sciences & Humanities · doi:10.62810/jssh.v2i2.30 · Cited by 2
“Bobula (2024) also points out that while AI offers tailored educational support, it must be used carefully to ensure that it does not replace the development of critical thinking and problem-solving abilities.”
From Insights to Impact: An Agentic Learning Intelligence Framework for Student-Centred Success
Lai, J. W., Qiu, W., & Lim, F. S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-99261-2_10 · Cited by 2
Guedes, T. S. D. V., Rupprecht, P., & Rodenas, I. (2026) · Journal of Organizational Change Management · doi:10.1108/jocm-01-2026-0095 · Cited by 4
Role of Generative Artificial Intelligence and Work Environment on the Motivation of Workers
Dharmawan, A. P., Hartantono, N., & Lasmy, L. (2024) · International Conference on Creative Communication and Innovative Technology (ICCIT) · doi:10.1109/iccit62134.2024.10701197 · Cited by 2
Introducción de inteligencia artificial en la evaluación de asignaturas de teoría e historia
Nadal, M. F., & Mora, E. S. (2024) · Jornadas sobre Innovación Docente en Arquitectura (JIDA) · doi:10.5821/jida.2024.13259 · Cited by 3
Goarty, R., & Gupta, K. P. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13690-7 · Cited by 5
“…with the integration of generative AI, including issues related to academic integrity (Zeb et al., 2024; Duah & McGivern, 2024; Ali & Aysan, 2025; Bobula, 2024 ), concerns regarding the quality and reliability of AI-generated content (Ali & Aysan, 2024; Liu & Xiao, 2024 ), the credibility of…”
A survey of American public health faculty AI competency & readiness
Lord, E., Heboyan, V., Huang, H., Tracy, D., & De Leo, G. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2654902 · Cited by 1
Enhancing Employee Motivation Through AI: The Role of Corporate Culture in the Workplace
Prakash, S., Pavithrashree., S., Gokul., S., & Raja.K, K. (2024) · International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) · doi:10.1109/icses63760.2024.10910789 · Cited by 2
“All incentive theories do not change with employees’ needs or corporate culture [2].”
Exploring Perceptions of Learners Towards Using ChatGPT: A Qualitative Study
Sarkar, P. D. (2025) · Dibon Journal of Education · doi:10.64169/dje.68 · Cited by 1
Salsabilla, D., Kesuma, M. E.-K., Gunawan, I., & Ashari, A. (2025) · KnE Social Sciences · doi:10.18502/kss.v10i14.19136 · Cited by 1
Mutahar, A. M., Al-Sharafi, M. A., Shyyab, Y., & Zouria, A. (2025) · Journal of Organizational Change Management · doi:10.1108/jocm-03-2025-0278 · Cited by 1
Wilkins, B. P., & Djouma, E. (2025) · British Journal of Clinical Pharmacology · doi:10.1002/bcp.70345 · Cited by 1
“82 Further, a reflective piece by Brahim-Said describes how the use of AI, Augmented Reality (AR) and Virtual Reality (VR) have been essential to their academic success as a neurodiverse individual by allowing them to process and understand complex information.”
Designing Education for the AI Era: Principles for Integrating LLMs in Pedagogy
Biscalchin, A., Sarkheyli-Hägele, A., Eriksson, J., & Vogel, B. (2025) · International Conference on Software, Telecommunications and Computer Networks (SoftCOM) · doi:10.23919/softcom66362.2025.11197458 · Cited by 2
The Double-Edged Sword of Generative AI: Enhancing or Hindering Students’ Intellectual Growth?
Razzak, N. L. A. (2025) · IEEE International Conference on Emerging Trends in Engineering and Computing (ETECOM) · doi:10.1109/etecom66111.2025.11319048 · Cited by 1
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Baig, M., & Paparo, J. (2026) · Teaching in Higher Education · doi:10.1080/13562517.2026.2663440 · Cited by 2
AI or nay? How students balance the benefits and limitations of generative AI in higher education
Hands, C., Barton, C., Coulby, C., Saw, Q. Q., FARRELL, A., Stevens, C., Ellison, C., Saunders, S., & Lindsay, R. (2026) · Developing Academic Practice · doi:10.3828/dap.2026.16 · Cited by 1
Embedding Generative AI as a digital capability into a year-long MSc skills programPreprint
Smith, D. P., Sokoya, D., Moore, S., Okonkwo, C., Boyd, C., Lacey, M. M., & Francis, N. (2024) · Research Square · doi:10.21203/rs.3.rs-5204546/v1 · Cited by 1
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Nguyen, T. T., & Nguyen Huynh Hoai, P. (2026) · Proceedings of the 14th International Conference on Advances in Information Technology · doi:10.1145/3816713.3820249
Applying Technology for Alternative Learning and Instructional Design
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ЭТИЧЕСКИЕ АСПЕКТЫ ИСПОЛЬЗОВАНИЯ GPT В ОБРАЗОВАНИИ: ПСИХОЛОГИЧЕСКИЙ РАКУРС.Preprint
Лукинова, П. (2025) · doi:10.24108/preprints-3113468
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Towards Neutralizing a Threat in the University
Chibike, J. J., Salani, J., & Ndlovu, I. D. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-7873-1.ch014
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De lo técnico a lo pedagógico: formación docente integral en IA universitaria
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Inteligencia artificial y enseñanza en Ingeniería: una revisión sistemática de la literatura
Garrido, Y. C., Barragán, A. J., Enrique, J. M., Aquino, A., Martínez, M., & Andújar, J. M. (2025) · Jornadas de Automática · doi:10.17979/ja-cea.2025.46.12180 · Cited by 1
On the Use of Large Language Models for Improving Student and Staff Experience in Higher Education
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“Meanwhile, Bobula [7] presented a comprehensive review of LLM challenges and opportunities, advocating for institutional readiness and ethical AI deployment.”
Ramírez, M. I. T., Laveaga, J. A., & López, H. L. L. (2025) · Revista NeyArt · doi:10.61273/neyart.v3i3.147 · Cited by 1
Custom AI Ethics and Literacy Frameworks (Phase 4)
Woodring, D. (2026) · Springer eBooks · doi:10.1007/978-3-032-05205-6_16
Design and Implementation of a Context-Aware AI Scaffolding System for IoT Engineering Education
Zhang, X., Cui, K., Sun, H., Bai, B., Feng, X., & Yu, L. (2025) · International Conference on Information Science and Education (ICISE-IE) · doi:10.1109/icise-ie68873.2025.11379074
Chemistry faculty perspectives on artificial intelligence and pedagogical change in higher education
Matthews, M. B., & Potter, D. W. (2026) · Discover Education · doi:10.1007/s44217-026-01250-1
“A comprehensive review of challenges, opportunities, and implications by Bobula [10] explored both the potential threats that AI poses to higher education, including risks to academic integrity, bias, and misinformation, and its transformative possibilities for pedagogy, institutional strategy, and…”
Random Knowledge Verification as a Scalable Assessment Strategy in AI Rich Higher EducationPreprint
Duc, T. M. (2026) · Research Square · doi:10.21203/rs.3.rs-9103013/v1
Demeter, L. A. (2026) · Journal of Online Graduate Education · doi:10.65201/001c.158636
Impact of AI and ML in K-12 and Higher Education
Darwish, D. A. M., & Kumar, S. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-0976-5.ch009
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Garcia, J., Souleiman, H. A., & SOULEIMAN, I. A. (2026) · Unveiling seven continents yearbook journal · doi:10.65326/u7y566770
Bariham, I., Abdul-Hafiz, I., & Abdulai, S. (2026) · Higher Education Research · doi:10.11648/j.her.20261103.12
Gandha, S. J., & Harefa, J. (2026) · IEEE Middle East and North Africa Communications Conference (MENACOMM) · doi:10.1109/menacomm69507.2026.11532818
Reframing Agency in AI-Mediated Education
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Guerra-Guerrero, C. O., Tass-Herrera, B., Reyes, G. V., & Cocón, F. (2026) · Media Literacy and Academic Research · doi:10.34135/mlar-26-01-02
Harnessing Digital Tools for Translanguaging in ESL Classrooms: A Systematic Literature Review
Yi, H. X. (2026) · AJELP The Asian Journal of English Language and Pedagogy · doi:10.37134/ajelp.vol14.1.2.2026
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Using Generative AI to Educate and Train SMEs in Adopting Renewable Energy in Regional Australia
Hassan, A. U., Chaudhry, G. M., & Gide, E. (2026) · International Conference on Information Technology Based Higher Education and Training (ITHET) · doi:10.1109/ithet69978.2026.11585089
“[27][38][42][43].”
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Policy Ambiguity and Academic Integrity: Student Experiences of AI Governance in Higher Education
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Educators' Perspectives on Generative AI Use in Nursing Education: Friend or Foe?
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Cognitive Externalization and Generative AI in Higher Education
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Two-Year Repeated Cross-Sectional Study of AI Integration and Learning in Global Game Jams
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Generative AI in Academia: Student Perspectives
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Metadata
- Authors
- Bobula, M.
- Affiliations
- University of the West of England (GB)
- Corresponding author
- Bobula, Michal
- ORCID
- Bobula, Michal
- Venue
- Journal of Learning Development in Higher Education
- Published
- 2024-03-27
- Issue
- 30
- ISSN
- 1759-667X (ISSN-L)
- OpenAlex
- W4393226952
- Semantic Scholar
- c181109333f0a05719db08945ab95d9950a03c3c
- Cited by
- 113
- References
- 52
- Citations by year
- 2026: 56 · 2025: 45 · 2024: 12
- FWCI
- 42.49
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Journal of Learning Development in Higher Education (publishedVersion, cc-by)
- UWE Research Repository (UWE Bristol) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- generative AI, large language models, ChatGPT, higher education, educational assessment, academic integrity, privacy concerns, misinformation, diversity, equity, and inclusion, copyright, algorithmic bias, AI skills development, responsible AI adoption
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-08, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-08 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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