The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index; none of the 11 sources its claims cite is checked for support yet.
Challenges for higher education in the era of widespread access to generative AI
Open accessAbstract
The aim of this paper is to discuss the role and impact of generative artificial intelligence (AI) systems in higher education. The proliferation of AI models such as GPT-4, Open Assistant and DALL-E presents a paradigm shift in information acquisition and learning. This transformation poses substantial challenges for traditional teaching approaches and the role of educators. The paper explores the advantages and potential threats of using generative AI in education and necessary changes in curricula. It further discusses the need to foster digital literacy and the ethical use of AI. The paper’s findings are based on a survey conducted among university students exploring their usage and perception of these AI systems. Finally, recommendations for the use of AI in higher education are offered, which emphasize the need to harness AI's potential while mitigating its risks. This discourse aims at stimulating policy and strategy development to ensure relevant and effective education in the rapidly evolving digital landscape.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
Walczak surveys university students on their use and perception of generative AI and argues that widespread access to tools like GPT-4 and DALL-E disrupts higher education's traditional role in knowledge transfer and skill development. The paper recommends that universities teach AI literacy, critical verification of AI output, and preserve knowledge-based examination while preparing students for lifelong self-learning.
Key findings
- Of ~1,000 students contacted, 143 responded; 81% reported prior use of online AI systems and 79% had used ChatGPT. The most common purpose was "out of curiosity" (88%), followed by "finding a solution to a problem" (63%), "information search" (62%) and "programming" (52%); "generating an essay/abstract" and "translation" were less frequent.
- Only 2% of students declared complete trust in AI-generated content; 24% said they always scrutinize AI-produced data and facts, and 73% verify via search engines (27% via Wikipedia). Yet when shown a GPT-3.5 biography of Adam Mickiewicz containing a factual error, only 21% identified it as false while 50.7% failed to do so; among students who claimed to always verify, 45.7% still missed the error.
- 31% of students thought AI systems are currently mature enough for educational integration, but 76% agreed on their future utility; 55% advocated integrating AI into education with instruction on its use, and 51% were reluctant to have exams graded by AI.
- Better-performing students appeared to use AI less: in the "very good" cohort, 58% declared no use of AI in education (including 20% who tried and stopped). 89% of students knew AI does not think like a human.
Methods
- A survey of university students from diverse institutions and disciplines (Computer Science, Econometrics, Cloud Solutions, IoT, E-Business, Industry 4.0, Management, Administration, Business Psychology, HRM); 143 of about 1,000 responded. 36% were bachelor's students, 64% master's; 43% female, 57% male.
- The survey covered chatbot interaction preferences, purposes of ChatGPT use, other AI systems used, frequency of AI use for academic tasks, opinions on AI in education, trust in AI content, and a test in which students judged a GPT-3.5-generated biography containing a deliberate factual error.
- Results are reported as descriptive percentages and cross-tabulations (e.g., declared trust vs. actual error detection; AI use frequency vs. self-assessed performance). The paper also reviews generative AI architectures (VAEs, GANs, Transformers, GPT, WaveNet) and discusses hallucinations, authorship and originality.
Limitations
- The paper itself notes that the sample of students declaring very poor performance was too small to draw statistically significant conclusions, and that it is premature to draw definitive conclusions about AI's future trajectory.
- Evident from the text: the survey response rate is low (143 of ~1,000, ~14%), the sample is concentrated in computer-science-adjacent fields at the author's institution, and the analysis is largely descriptive with no inferential statistics reported.
- Evident from the text: the paper's own example of GPT hallucination concerns the author's personal biography, and the recommendations are framed as preliminary rather than tested interventions.
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Claims the paper makes
50 Own interpretation · 43 Own result · 26 No basis given · 8 Not checked yet · 2 Own method
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and a mere 1% believe that AI emulates human cognition.”
§ Challenges for higher education in the era of widespread access to Generative AI > out of curiosity
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — 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, the use of GPT during class sessions appears to be less frequent as it was reported by only 22% of respondents.”
§ Challenges for higher education in the era of widespread access to Generative AI > out of curiosity
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A result the paper reports from its own data — 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 terms of understanding the cognitive function of AI 89% of students are aware that AI does not think like a human”
§ Challenges for higher education in the era of widespread access to Generative AI > out of curiosity
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In the paper
“Conversely 20% of students are in favour of their permitted use but without active promotion.”
§ Challenges for higher education in the era of widespread access to Generative AI > out of curiosity
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Only 2% of students declared complete trust in the content generated by AI systems (Figure 9, left).”
§ Challenges for higher education in the era of widespread access to Generative AI > 3.2. Analysis of the results
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Notably 81% of the surveyed students reported prior usage of online AI systems.”
§ Challenges for higher education in the era of widespread access to Generative AI > 3.1. Results of the survey
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“23% expressed reservations”
§ Challenges for higher education in the era of widespread access to Generative AI > out of curiosity
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In the paper
“'Understanding a complex issue' was chosen by 45% of the students.”
§ Challenges for higher education in the era of widespread access to Generative AI > out of curiosity
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“while 22% do express such concerns.”
§ Challenges for higher education in the era of widespread access to Generative AI > out of curiosity
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“However, when contemplating the future utility of AI systems in education a significant 76% of students demonstrated strong agreement.”
§ Challenges for higher education in the era of widespread access to Generative AI > out of curiosity
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- Probably not a substantial claim. 73
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In the paper
“20% remained neutral.”
§ Challenges for higher education in the era of widespread access to Generative AI > out of curiosity
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In the paper
“10% are unsure”
§ Challenges for higher education in the era of widespread access to Generative AI > out of curiosity
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In the paper
“Interestingly 65% of students are not worried about job displacement due to AI”
§ Challenges for higher education in the era of widespread access to Generative AI > out of curiosity
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In the paper
“This belief is amplified when considering future scenarios with a substantial 86% of students envisaging that AI systems will serve professional functions in the future.”
§ Challenges for higher education in the era of widespread access to Generative AI > out of curiosity
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In the paper
“A total of 38% of students consider that AI systems have reached a sufficient level of maturity for professional applications albeit in specialized domains.”
§ Challenges for higher education in the era of widespread access to Generative AI > out of curiosity
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In the paper
“87.5% who use AI often or very often”
§ Challenges for higher education in the era of widespread access to Generative AI > 3.2. Analysis of the results
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“This opinion has been expressed by 55.24% of all participants”
§ Challenges for higher education in the era of widespread access to Generative AI > 3.2. Analysis of the results
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“61.9% of those who ever tried using AI”
§ Challenges for higher education in the era of widespread access to Generative AI > 3.2. Analysis of the results
plain- Asserted: passes — The authors put it forward as true.
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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”
§ Challenges for higher education in the era of widespread access to Generative AI > 3.2. Analysis of the results
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In the paper
“Hallucinations can be categorized broadly into closed-domain and opendomain hallucinations (Bubeck et al., 2023).”
§ Challenges for higher education in the era of widespread access to Generative AI > 2.2. Errors and hallucinations in content
What the sources say
- Not checked yetcites Bubeck et al., 2023
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In the paper
“In recent years Large Language Models (LLMs) have emerged as the dominant paradigm in the realm of text processing and generation, outperform-ing traditional methods and demonstrating unprecedented performance across a wide range of natural language processing tasks”
§ Challenges for higher education in the era of widespread access to Generative AI > 1.1.1. Large Language Models
What the sources say
- Not checked yetcites Brown et al., 2020
- Not checked yetcites Devlin et al., 2019
- Not checked yetcites Vaswani et al., 2017
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In the paper
“The model is able to generate realistically sounding voices or music by directly modelling waveforms with the use of a neural network trained with sample recordings”
§ Challenges for higher education in the era of widespread access to Generative AI > 1.3. Generation of sound and music
What the sources say
- Not checked yetcites van den Oord et al., 2016
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In the paper
“These generators employ deep learning techniques, specifically Variational Autoencoders (VAEs) (Kingma & Welling, 2014), Generative Adversarial Networks (GANs) (Goodfellow et al., 2014), and similar frameworks to synthesize high-quality, realistic images.”
§ Challenges for higher education in the era of widespread access to Generative AI > 1.2. Generation of images and video
What the sources say
- Not checked yetcites Kingma & Welling, 2014
- Not checked yetcites Goodfellow et al., 2014
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A total of 143 students out of about 1,000 responded to this survey.”
§ Challenges for higher education in the era of widespread access to Generative AI > 3. A survey on the use of AI by students
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In the paper
“The accuracy of the automatic detection of AI-generated text is quite good but the tools tend to produce too many false positives for texts written by nonnative speakers (Liang et al., 2023).”
§ Challenges for higher education in the era of widespread access to Generative AI > 2.3. Authorship and originality of content
What the sources say
- Not checked yetcites Liang et al., 2023
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Tools exist for textual content (e.g., OpenAI AI Classifier, OriginalityAI, GPZERO) and multimedia content (e.g., Illuminarty) to detect AI-generated content.
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
“With the proper amount of knowledge students can then utilise this knowledge to develop their critical thinking abilities.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.1. Transfer of knowledge
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In the paper
“Communication skills also apply to a conversation between a human and an AI.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.4. Use of AI tools
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In the paper
“A risk is that humans will rely on AI so much that they will deprive themselves of knowledge and reduce their ability to think logically and reason.”
§ Challenges for higher education in the era of widespread access to Generative AI > 4. Consequences for higher education
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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 addition to fostering cognitive development the education process also plays a key role in shaping interpersonal skills, emotional intelligence and ethical values.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.3. Social and communication skills
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In the paper
“A strong knowledge base also equips individuals to detect inconsistencies or inaccuracies thus minimising the risk of accepting false information as truth.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.1. Transfer of knowledge
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In the paper
“Humans are predicted to assume roles as managers and trainers of these program robots prompting a shift in demand from low-skilled white-collar employees who were traditionally educated to serve clients to those possessing high-level and interdisciplinary skills required for taking care of”
§ Challenges for higher education in the era of widespread access to Generative AI > 4. Consequences for higher education
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In the paper
“In order to distinguish facts from hallucinations an individual must possess personal knowledge.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.1. Transfer of knowledge
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Furthermore, creativity which is an essential aspect of intellectual development flourishes in an educational environment that encourages exploration and divergent thinking.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.2. Stimulating intellectual abilities
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In the paper
“This challenge will increase as AI improves and makes fewer errors.”
§ Challenges for higher education in the era of widespread access to Generative AI > 4. Consequences for higher education
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In the paper
“However, the rapid development of generative AI makes it more likely that white-collar jobs are also at risk.”
§ Challenges for higher education in the era of widespread access to Generative AI > 4. Consequences for higher education
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In the paper
“Moreover, incorporating AI literacy into higher education curricula can contribute to fostering a culture of responsible AI use.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.4. Use of AI tools
plainconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“These challenges while demanding and time-consuming ultimately contribute to the development of various skills and competencies.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.2. Stimulating intellectual abilities
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In the paper
“To achieve this it is necessary that the educational process presents challenges that require from students significant effort and perseverance to overcome.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.2. Stimulating intellectual abilities
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Under such circumstances it becomes exceedingly easy to make erroneous decisions which may bear significant consequences.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.1. Transfer of knowledge
boostedconditional- Asserted: passes — The authors put it forward as true.
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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
“The field of explainable AI which seeks to supplement machine learning with techniques that offer insight into these correlations is still in its infancy.”
§ Challenges for higher education in the era of widespread access to Generative AI > 4. Consequences for higher education
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. 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
“The communication skills include, in particular the ability to ask the AI pertinent questions, sometimes repeatedly, sometimes phrased differently and sometimes using different keywords in order to get results that are as truthful as possible to understand the problem being described.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.4. Use of AI tools
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.
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
“Engaging in a rigorous educational process in which individuals deal with complex ideas and problems stimulates critical thinking, problem solving and creativity.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.2. Stimulating intellectual abilities
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.
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 acquisition of facts from credible sources aids in fostering effective communication skills.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.3. Social and communication skills
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.
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
“By emphasizing critical analysis of AI-generated content academic institutions can help students develop a nuanced understanding of the strengths and limitations of AI solutions.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.4. Use of AI tools
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.
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
“When students meet the established requirements and attain a certain level of education they become more attractive to employers.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.5. Selection and motivation
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
“Education plays an important role in supporting the growth and intellectual development of individuals.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.2. Stimulating intellectual abilities
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.
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
“This is primarily because AI models and especially those based on machine learning are founded on correlation rather than cause-and-effect relationships.”
§ Challenges for higher education in the era of widespread access to Generative AI > 4. Consequences for higher education
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. 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
“When students are presented with challenging tasks that require them to think outside the box they can exploit their creative potential.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.2. Stimulating intellectual abilities
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.
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
“Unfortunately the increase in productivity, global accessibility, multilingual capabilities and personalization offered by software robots is likely to reduce the overall need for human labour in the market.”
§ Challenges for higher education in the era of widespread access to Generative AI > 4. Consequences for higher education
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. 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
“However, it should be noted that the human decision-making process can take a relatively long time and despite the well-organized cognitive mechanisms humans are prone to errors.”
§ Challenges for higher education in the era of widespread access to Generative AI > 4. Consequences for higher education
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: 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.
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
“The most important educational challenge arising from unrestricted access to generative AI solutions such as ChatGPT is the blurring of boundaries between factual information and generated hallucinations.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.1. Transfer of knowledge
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.
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
“This form of mental exercise promotes the development of cognitive abilities as well as adaptability and resilience in the face of adversity.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.2. Stimulating intellectual abilities
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. 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
“This not only contributes to personal growth but also promotes collective progress in the pursuit of knowledge and innovation.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.3. Social and communication skills
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.
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 use of AI may be an excellent opportunity for developing creative thinking.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.2. Stimulating intellectual abilities
hedgedconditional- 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.
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
“Still one major error made by AI undetected by humans beforehand may have catastrophic consequences.”
§ Challenges for higher education in the era of widespread access to Generative AI > 4. Consequences for higher education
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.
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
“By setting specific requirements for each level of education universities establish a standardized framework that allows for consistent measurement of knowledge and skills.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.5. Selection and motivation
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
“Conversely artificial intelligence primarily operates based on statistical patterns.”
§ Challenges for higher education in the era of widespread access to Generative AI > 4. Consequences for higher education
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. 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
“Although one may peruse various sources of information for this purpose the increasing advancement of artificial intelligence raises the possibility that these sources may be contaminated with hallucinations.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.1. Transfer of knowledge
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.
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
“Only an individual equipped with knowledge can utilise artificial intelligence in a sensible manner thus minimising the risk of confusing facts with hallucinations and drawing erroneous conclusions.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.1. Transfer of knowledge
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
“By adopting this mindset individuals are more likely to persevere in their work and view setbacks as opportunities for personal growth rather than as insurmountable barriers.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.2. Stimulating intellectual abilities
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
“By engaging in cooperative learning experiences, participating in diverse learning environments and reflecting on moral and ethical dilemmas students can develop empathy, cultural competence and a strong sense of personal responsibility.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.3. Social and communication skills
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.
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 potential risk for decision-makers stems from the confusion of recommendations derived from statistical analysis with those based on logical reasoning.”
§ Challenges for higher education in the era of widespread access to Generative AI > 4. Consequences for higher education
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: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
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
“This understanding will enable them to make informed decisions about when and how to use these tools in their academic pursuits and professional careers.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.4. Use of AI tools
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: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
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
“With a good understanding of a subject individuals are better equipped to engage in meaningful conversations, exchange ideas and collaborate with others in their academic or professional fields.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.3. Social and communication skills
plaincorrelational- 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.
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, the advent of generative AI promises to revolutionize the nature of intellectual labour, often referred to as”
§ Challenges for higher education in the era of widespread access to Generative AI > 4. Consequences for higher education
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.
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 this trend continues it is anticipated that the labour market will exhibit a declining demand for low-skilled production workers and conversely an increasing demand for highly skilled engineers tasked with operating, maintaining, integrating, developing, and programming robots.”
§ Challenges for higher education in the era of widespread access to Generative AI > 4. Consequences for higher education
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
“When universities establish clear and challenging requirements students are motivated to continuously improve their knowledge and skills to meet those expectations.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.5. Selection and motivation
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
“The high statistical probability of a particular phenomenon reoccurring derived from past data does not guarantee its future repetition.”
§ Challenges for higher education in the era of widespread access to Generative AI > 4. Consequences for higher education
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: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
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
“What is more the process of tackling difficult and complex tasks fosters the belief that abilities can be developed through effort and learning.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.2. Stimulating intellectual abilities
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
“An important role of universities remains to set requirements appropriate to a given level of education and verify the attainment of this level by students.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.5. Selection and motivation
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: passes — The paper gives a 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. 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
“Consequently this may lead to the propagation of misinformation and hinder the acquisition of accurate knowledge emphasising the need for critical evaluation and verification of AI-generated content in educational settings.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.1. Transfer of knowledge
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
“All this influences the positive motivation of students to work and helps promote talented individuals.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.5. Selection and motivation
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: passes — The paper gives a 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. 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
“A student enters a university when he/she is 19 years old, leaves a university when he/she is 24 years old, and has to work for 45 years-until he/she reaches the age of 69 (in the near future).”
§ Challenges for higher education in the era of widespread access to Generative AI > Conclusions
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: 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.
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
“Consequently the development of mechanisms to distinguish between factual information and hallucinations becomes imperative which currently relies on human involvement, particularly individuals who possess extensive knowledge.”
§ Challenges for higher education in the era of widespread access to Generative AI > Conclusions
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.
Probably not a substantial claim. 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 nuances of language, cultural references and context-specific elements may not always be accurately captured by AI systems leading to potential misinterpretations or misunderstandings.”
§ Challenges for higher education in the era of widespread access to Generative AI > Conclusions
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.
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
“Technology kills old jobs and creates new ones.”
§ Challenges for higher education in the era of widespread access to Generative AI > Conclusions
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.
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
“This creates organizational and competency gaps that continue to grow.”
§ Challenges for higher education in the era of widespread access to Generative AI > Conclusions
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.
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
“Training AI models on hallucinatory outputs may intensify the generation of such hallucinations potentially leading to a decline in the quality of the generated texts.”
§ Challenges for higher education in the era of widespread access to Generative AI > Conclusions
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: unclear — It is unclear why the paper says 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
“AI can help people keep up with changes by anticipating how their work environment and required competencies will change and assisting them in the necessary development.”
§ Challenges for higher education in the era of widespread access to Generative AI > Conclusions
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.
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
“However, it is important to note that GPT models have been trained on data collected until 2021 while the corpus of available data continues to grow and evolve.”
§ Challenges for higher education in the era of widespread access to Generative AI > Conclusions
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: 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.
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
“No one at a university can predict what to teach students now that will be useful to them in their job 35-45 years from now, i.e., for the last ten years of their working life.”
§ Challenges for higher education in the era of widespread access to Generative AI > Conclusions
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: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
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, a university that sets appropriate educational requirements fosters a competitive environment among its students.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.5. Selection and motivation
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.
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
“Due to possible hallucinations and uncertainty about the veracity of the knowledge offered by AI however it will be necessary to work with competent and knowledgeable people whose pool of knowledge should remain at the university.”
§ Challenges for higher education in the era of widespread access to Generative AI > Conclusions
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.
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
“This increases their chances of securing well-paid jobs and advancing in their careers.”
§ Challenges for higher education in the era of widespread access to Generative AI > 5.5. Selection and motivation
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. 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
“Universities will be unable to prepare students in advance for new jobs so they should provide students with the capabilities to quickly update and develop new skills.”
§ Challenges for higher education in the era of widespread access to Generative AI > Conclusions
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: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. 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
“On the one hand notable advancements in AI algorithms as exemplified by the comparison between GPT-3 and GPT-4, indicate potential improvements in the quality of AI-generated content.”
§ Challenges for higher education in the era of widespread access to Generative AI > Conclusions
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: 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
“While AI models aim to generate coherent and contextually relevant output the interpretation of meaning and intent remains subjective and reliant on human understanding.”
§ Challenges for higher education in the era of widespread access to Generative AI > Conclusions
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.
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
“The world is developing unevenly.Technology and especially AI is growing exponentially, organizations are growing logarithmically, and education is growing by small leaps.”
§ Challenges for higher education in the era of widespread access to Generative AI > Conclusions
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
“Furthermore, the subjective nature of interpretation introduces a level of variability among individuals as different people may perceive and understand AI-generated content in diverse ways.”
§ Challenges for higher education in the era of widespread access to Generative AI > Conclusions
hedgeddirect- 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: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
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 addition to concerns about the quality of AI-generated content the subjective interpretation of such content poses another significant challenge.”
§ Challenges for higher education in the era of widespread access to Generative AI > Conclusions
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.
Probably not a substantial claim.
The paper relies on this without support.
GPT-4 can analyse as many as 32,768 tokens (about 24,000 words).
“Reportedly, GPT-4 is able to analyse as many as 32,768 tokens (about 24,000 words).”
The paper relies on this without support.
Generative AI systems are widely accessible to higher education students.
“The availability of generative AI systems such as the GPT-4, Open Assistant, DALL-E, Midjourney, and many others has dramatically changed the landscape of information acquisition and learning.”
The paper relies on this without support.
Easy access to generative AI leads to academic dishonesty.
“Then the focus is on the threats posed by easy access to automatically generated content such as academic dishonesty and the possible erosion of critical thinking skills.”
The paper relies on this without support.
Easy access to generative AI leads to the possible erosion of critical thinking skills.
“Then the focus is on the threats posed by easy access to automatically generated content such as academic dishonesty and the possible erosion of critical thinking skills.”
The paper relies on this without support.
The style of a given artist can very easily be reproduced by an AI system.
“The style of a given artist can very easily be reproduced by an AI system (cf.Section 1.2).”
The paper relies on this without support.
A person without knowledge is unable to distinguish between the right and wrong parts of the generated text.
“A person without knowledge is unable to distinguish between the right and wrong parts of the generated text.”
The paper relies on this without support.
Hallucinations are often intertwined with accurate information and conveyed in a persuasive and self-assured manner, rendering their detection difficult without careful scrutiny and rigorous fact verification.
“Hallucinations are often intertwined with accurate information and conveyed in a persuasive and self-assured manner rendering their detection difficult without careful scrutiny and rigorous fact verification.”
The paper relies on this without support.
Open-domain hallucinations are more challenging and require comprehensive research to identify.
“Contrarily open-domain hallucinations such as those presented above are a more challenging case which requires comprehensive research to identify.”
The paper relies on this without support.
AI-generated content is often indistinguishable from human-created content for a typical human reader.
“AI-generated content is often indistinguishable from human-created content for a typical human reader.”
The paper relies on this without support.
To some extent it is possible to automatically detect AI-generated content.
“However, to some extent it is possible to automatically detect such content.”
The paper relies on this without support.
Tools exist for textual content (e.g., OpenAI AI Classifier, OriginalityAI, GPZERO) and multimedia content (e.g., Illuminarty) to detect AI-generated content.
“Tools exist for textual content (e.g., OpenAI AI Classifier, OriginalityAI, GPZERO) and multimedia content (e.g., Illuminarty).”
The paper relies on this without support.
Automatic identification of AI-generated images and videos is more challenging than detecting text, but this type of multimedia content is easier to distinguish by humans.
“Automatic identification of AI-generated images and videos is more challenging than detecting text, but at the same time this type of multimedia content is easier to distinguish by humans.”
The paper relies on this without support.
Typical problems in AI-generated images include incorrect proportions of the human body, poorly represented hands and fingers and deformed background elements.
“Typical problems in AI-generated images include incorrect proportions of the human body, poorly represented hands and fingers and deformed background elements.”
The paper relies on this without support.
The survey respondents are representative of the broader student population.
“Only 2% of students declared complete trust in the content generated by AI systems (Figure 9, left).”
The paper relies on this without support.
The factual error in the GPT-3.5 biography is obvious.
“57.5% failed to identify the obvious error in the content.”
The paper relies on this without support.
The survey questions accurately measure students' trust and attentiveness.
“First an analysis was carried out to determine to what extent students trust content generated by AI and if they can really identify obvious problems with automatically generated content.”
The paper relies on this without support.
The overall sample size of the survey is sufficient to draw statistically significant conclusions.
“The sample is too small to draw any statistically significant conclusions from this.”
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Another relevant aspect is cognitive dependence, where excessive use of GenAI can inhibit the development of critical thinking skills and autonomy in students (Walczak and Cellary, 2023).”
From Ethical and regulatory challenges of Generative AI in education: a systematic review (2025) · cites this paper as Walczak and Cellary, 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“It is noted that the digital divide can limit equitable access to these technologies in disadvantaged contexts (Walczak and Cellary, 2023).”
From Ethical and regulatory challenges of Generative AI in education: a systematic review (2025) · cites this paper as Walczak and Cellary, 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The development and operation of GenAI carry significant environmental costs, including high energy and resource consumption, and raise concerns about labor exploitation and job displacement.”
From MAPPING GENERATIVE AI'S ETHICAL ISSUES IN HIGHER EDUCATION: A FELT-GUIDED SYSTEMATIC REVIEW [PEMETAAN ISU ETIKA GENERATIVE AI DI PENDIDIKAN TINGGI: TINJAUAN SISTEMATIS BERPANDUAN FELT] (2025) · cites this paper as Walczak & Cellary, 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This supports Walczak and Cellary's (2023) study that found 50% of participants using generative AI for writing-based tasks.”
From A Survey on Student Use of Generative AI Chatbots for Academic Research (2024) · cites this paper as Walczak and Cellary's (2023)
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Walczak and Cellary [60] also highlighted the need for support in this new digital literacy.”
From Generative AI in Higher Education: Balancing Innovation and Integrity (2025) · cites this paper as [60]
The cited source says what the claim attributes to it.
In the citing paper
“Students falsely claiming AI-generated work as their own violates ethical standards and undermines the principle of academic authenticity.”
From MAPPING GENERATIVE AI'S ETHICAL ISSUES IN HIGHER EDUCATION: A FELT-GUIDED SYSTEMATIC REVIEW [PEMETAAN ISU ETIKA GENERATIVE AI DI PENDIDIKAN TINGGI: TINJAUAN SISTEMATIS BERPANDUAN FELT] (2025) · cites this paper as Walczak & Cellary, 2023
The cited source says what the claim attributes to it.
In the citing paper
“Finally, insufficient digital literacy can lead to misuse or misinterpretation of GenAI's capabilities and limits (Walczak and Cellary, 2023).”
From Ethical and regulatory challenges of Generative AI in education: a systematic review (2025) · cites this paper as Walczak and Cellary, 2023
The cited source says what the claim attributes to it.
In the citing paper
“AI's”
From MAPPING GENERATIVE AI'S ETHICAL ISSUES IN HIGHER EDUCATION: A FELT-GUIDED SYSTEMATIC REVIEW [PEMETAAN ISU ETIKA GENERATIVE AI DI PENDIDIKAN TINGGI: TINJAUAN SISTEMATIS BERPANDUAN FELT] (2025) · cites this paper as Walczak & Cellary, 2023
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Universities risk losing intellectual property (IP) rights or disclosing confidential research information if it is fed into institutionally unsupported AI applications.”
From MAPPING GENERATIVE AI'S ETHICAL ISSUES IN HIGHER EDUCATION: A FELT-GUIDED SYSTEMATIC REVIEW [PEMETAAN ISU ETIKA GENERATIVE AI DI PENDIDIKAN TINGGI: TINJAUAN SISTEMATIS BERPANDUAN FELT] (2025) · cites this paper as Walczak & Cellary, 2023
Experimental — accuracy not yet measured
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Metadata
- Authors
- Walczak, K., & Cellary, W.
- Affiliations
- Poznań University of Economics and Business (PL)
- WSB Merito University in Poznan (PL)
- Venue
- Economics and Business Review
- Published
- 2023-07-20
- Volume
- 9
- Issue
- 2
- ISSN
- 1643-5877 (ISSN-L)
- 2450-0097
- 2392-1641
- OpenAlex
- W4385070262
- Semantic Scholar
- 6ea2e402d9d573b6303db35a8347b2a002074fd4
- Cited by
- 196
- References
- 31
- Citations by year
- 2026: 52 · 2025: 86 · 2024: 57 · 2023: 1
- FWCI
- 46.00
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Economics and Business Review/The Poznań University of Economics Review (publishedVersion, 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
- Social Sciences › Education › Digital Transformation of Higher Education
- Keywords
- generative AI, higher education, AI in teaching, GPT-4, digital literacy, AI ethics, curriculum change, student perceptions of AI, role of educators, AI policy in education
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 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (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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