Introduction

The impressive developments and uptakes of Artificial Intelligence (AI) technologies in the last few years have consistently reshaped how we live, learn and work. The overnight transformations are profound and their real, long-term impacts are yet to be analyzed, understood and mitigated. The rapidly evolving AI landscape brings not only tremendous opportunities but also unimaginable challenges. This research aims to provide an overview of both sides of the coin, raising questions on how to approach and manage AI experiences in education to pave the way for more constructive and safer endeavors.

Since the beginning of times, teachers formed the core of the evolution of civilizations. Their constant efforts and sacrifices, their curiosity and insisting efforts to provide the best and the newest to their students have shaped the human mind to thrive. From this perspective, teachers’ responses to AI exceeded expectations. Research provides significant evidence on different levels and domains of how AI has been massively embraced by teachers from primary school to adult education, and from language learning to engineering and medicine (Zheng & Yanf, 2024; Adbelwhab et al., 2024; Yim, 2024; Wang et al., 2025; Xu & Baghaei, 2025). Teachers’ enthusiasm has opened up new perspectives and possibilities built on the new affordances. AI has brought. These capabilities can enhance human performance and enable humans to excel in high-expertise domains that require specialized knowledge and skills or they can assist humans in reducing tedious or dangerous tasks, creating safer and more motivational environments (Gnambs et al., 2025).

Therefore, preparing future generations requires a consistent and responsible understanding of the multi-faced implications the use of AI in education can bring. It is important to consider the fact that it takes time for competencies to mature, and both teachers and students need extensive support to achieve advanced AI literacy (Yim, 2024). Signaled that AI literacy is situated at the intersection of digital literacy, data literacy, computational thinking, and AI ethics, highlighting the need for transdisciplinary and interdisciplinary efforts that blend technological and societal approaches. Such perspectives require strategies and actions at the ecosystem level and supporting the integration of pedagogies and technologies across the learning ecosystem is a key priority to enable seamless learning (Looi et al., 2012; Hambrock et al., 2020).

AI has the potential to fuel the development of the learning ecosystem faster and further than any other technology has done before. To navigate a technology-driven future (Fu, 2025) and achieve an effective implementation, it is critical to be properly aware of and carefully assess the benefits and risks of AI. Such an analysis should start from the essence of AI and what it has to offer. AI algorithms build upon previous human experiences. They are constructed to learn from these experiences and imitate them as efficiently as possible (Wang et al., 2025). The questions that remain are what AI has to offer and at what costs. The following sections explore the added value of AI and look into the potential risks that the use of AI can bring, with a focus on the educational contexts.

The Good

The Added Value of AI

Over the years, AI-based algorithms have proven to provide advanced analysis, as well as support and automated decisions and actions, in domains such as financial services (Khan et al., 2025) or managerial decision-making (Bagchi & Sharma, 2024).  From harvesting models developed by META to foundational and generative AI models, made AI promises bloom.

AI capabilities seemed to easily pave the way towards a futuristic vision of what education should be: personalized, interactive, immersive and accessible experiences. For example, if we are considering teacher support, AI tools can assume a significant number of tasks ranging from automatic grading to intelligent tutoring systems. Plenty of tools can assist both teachers and students by supporting smart content creation and by providing virtual assistants. h capabilities for education created new opportunities, and, at the same time, generated many expectations. The rapid evolution from conversational chatbots (ChatGPT developed by Open AI (n.d.) or BARD developed by Google (Gemini (chatbot), 2025)) and large language models (LaMDA (Collins & Ghahramani, 2021) and BARD developed by Google, GPT-4 developed by Open AI (n.d.))

At the end of 2024, there are tools for content creation (Jasper AI) or for image generation (Jasper Art) (Jasper, 2025), AI for art creation (Midjourney (n.d.), Adobe Firefly (2025), Suno AI (n.d.)), assistants for writing and editing (Grammarly) or for developers (GitHub Copilot), tools for real-time meeting transcription (Otter AI (n.d.)) or for research (Perplexity AI (n.d.)).

A quick look at the most popular AI tools generated based on the traffic volume showcases the outreach of such technologies (Figure 1). The interest is increasing, and more and more applications integrate AI-based features intending to make our lives easier.

Furthermore, the future looks promising! According to Gartner’s AI Hype Cycle that is a representation of the maturity, adoption metrics and business impact of AI technologies, composite AI surfaced as the foundation for future AI architectures and promises to create more adaptable and scalable solutions (Figure. 2).

The Bad

The Side Effects of AI

Switching from the role of an AI advocate to the role of an AI critic we can identify several concerns that have been raised in connection to the widespread use of AI tools. Some concern the students and the fact that they should not see AI tools as their answer to all their assignments. Some concern the teachers and the danger of AI tools being perceives as the absolute of human knowledge.

Cheating behaviors in the era of generative AI have been subject to extensive research (Lee et al., 2024; Playfoot et al., 2024; Klijn et al., 2025) that has attempted to identify solutions. Academic dishonesty is not new and tools, such as Turnitin® (n.d.), claim that they can detect text generated by ChatGPT (Sweeny, 2023). However, such tools cannot solve the problem of apparent veracity. Moreover, since the extensive use of AI tools has generated large quantities of data, the ability to fact-check and to limit distorted versions of the initial human input becomes more and more limited. Trustworthiness frameworks are required to model human-AI interactions.

Without a proper regulated data governance, it becomes nearly impossible to check if the resulting data is accurate, bias-free, and complete, and if it observes Intellectual Property Rights (IPRs) (Kostopolus, 2025). Strategies and specific actions are required as soon as possible to mitigate IPRs in the digital era (Lasisi & Tembe, 2025) and stop the tsunami effect of unregulated AI adoption.

Another critical issue that needs to be addressed concerns the high costs associated with AI use. As AI tools tend to be used, it has become necessary to reflect upon the real costs of such technologies, which are, by no means, low.

Headlines such as “When AI Fights Back: The Story of ChatGPT Attempting to Copy Itself” (DEVELOPIA, 2025) are meant to attract readers but can also create panic. Fortunately, the example above provides a good and solid explanation of this specific context, but this is not always the case. We can always start reasoning from the basics. For AI models to save themselves on a different machine they need to have access to significant resources. The fundamental element of any digital technology is access to electricity. Training large models consumes a lot of electricity. As stated by (Crownhart, 2024), using AI for certain tasks can come with a significant energy price tag that we should check and be aware of.

All these form examples of the challenges that AI brings along, and raise a signal that actions are required to address them timely and to find solutions before problems become more complex.

The Ugly

The Dark Side of AI

If we are to check the AI expectations vs. the AI reality we might end up with a significant, unexpected unbalance. This section provides a few reference examples, in an attempt to raise awareness on some critical issues that connect to the massive uptake of AI.

More and more fraudulent news and extensive forms of disinformation have started to affect our lives and the credibility of the whole media network could be jeopardized in the future (Ahmed et al, 2024). Fake online reviews are more and more common (Thao et al., 2024) and the credibility of information cannot be guaranteed (Dhiman et al., 2024). The development of methods for detecting and preventing fake news is essential (Alghamdi et al., 2024), but this will not suffice without a rigor, coordinated intervention from the educational organizations and decision-makers. The lack of timely regulation and limited automated fact-verification mechanisms have resulted in the proliferation of low-quality and false content (Alghamdi et al., 2024).

Social manipulation through AI algorithms has become the norm of our daily lives. We can see the increasing prominence of virtual influencers as a new strategy for influencer marketing (Kim & Wang, 2024), which can create the premises for propagating inaccurate digital content. What started as a good initiative can become a challenge in terms of ethics.

Deep fakes that represent realistic fake images formed through computational modification (Sharma et al., 2024) are a key rising challenge of the 21st century. 2024 has been living proof of how existing AI technologies can be used for disseminating false information by politicians (Gura et al., 2024) representing a real threat to fundamental rights and democracy.

Large amounts of data are concentrated in AI tools, and the lack of adequate data privacy regulation that can protect individuals from data privacy harm caused by AI can represent a real threat.

All these are concerning developments that must be tackled diligently to find suitable solutions to the upcoming challenges. Even if efforts are being made (European Parliament, 2023), constant and consolidated efforts are required to achieve visible, positive. Long-term results. The benefits that AI can bring are shadowed by the negative influence of the misuse of AI.

Conclusions

AI is not new. Moreover, it is all around us. We have smartphones, smart homes, and smart cars and all these bring undeniable benefits to our professional and private lives. It has, however, become timely that education prepares us better for the world we live in.

The rapid changes in our AI landscape significantly impact the learning ecosystem. We need to learn to improve of our decision-making capabilities when it comes to using AI. Through education we can learn how to benefit from the use of AI and, at the same time, how to fight against the misuse of AI, avoid risks and understand how to be proactive.

References

Abdelwahab, S. I., Taha, M. M. E., Farasani, A., Jerah, A. A., Abdullah, S. M., Aljahdali, I. A., Oraibi, B., Alfaifi, H. A., Alzahrani, A. H., Oraibi, O., Babiker, Y., & Hassan, W. (2024). Artificial intelligence in nursing education: A bibliometric analysis of trends, challenges, and future directions. Teaching and Learning in Nursing. https://doi.org/10.1016/j.teln.2024.11.018

Adobe (2025). Create with Adobe Firefly generative AI. [Software application]. https://www.adobe.com/ca/products/firefly.html

Ahmed, K., Khan, M. A., Haq, I., Al Mazroa, A., M. S., Innab, N., Alajmi, M., & Alkahtani, H. K. (2024). Social media’s dark secrets: A propagation, lexical and psycholinguistic oriented deep learning approach for fake news proliferation. Expert Systems with Applications, 255(C), 124650. https://doi.org/10.1016/j.eswa.2024.124650

Alghamdi, J., Lin, Y., Luo, S. (2024). Unveiling the hidden patterns: A novel semantic deep learning approach to fake news detection on social media. Engineering Applications of Artificial Intelligence, 137(B), 109240. https://doi.org/10.1016/j.engappai.2024.109240

Bagchi, S. N., & Sharma, R. (2024). Managerial decision making and AI: A decision canvas approach. Business Horizons. https://doi.org/10.1016/j.bushor.2024.12.001

Cardillo, A. (2024, October 1). 20 Most Popular AI Tools Ranked (September 2024). [Web log post]. Exploding Topics. https://explodingtopics.com/blog/most-popular-ai-tools

Chen, Y., Wang, H., Hill, S. R., & Li, B. (2024). Consumer attitudes toward AI-generated ads: Appeal types, self-efficacy, and AI’s social role. Journal of Business Research, 185, 114867. https://doi.org/10.1016/j.jbusres.2024.114867

Collins, E., & Ghahramani, Z. (2021, March 18). LaMDA: our breakthrough conversation technology. [Web log post]. The Keyword. https://blog.google/technology/ai/lamda/

Crownhart, C. (2024, May 23). AI is an energy hog. This is what it means for climate change. MIT Technology Review. https://www.technologyreview.com/2024/05/23/1092777/ai-is-an-energy-hog-this-is-what-it-means-for-climate-change/

DEVELOPIA. (2025, January 4). When AI Fights Back: The Story of ChatGPT Attempting to Copy Itself [Post]. https://developia.substack.com/p/when-ai-fights-back-the-story-of

Dhiman, P., Kaur, A., Gupta, D., Juneja, S., Nauman, A., & Muhammad, G. (2024). GBERT: A hybrid deep learning model based on GPT-BERT for fake news detection. Heliyon, 10(16), e35865. https://doi.org/10.1016/j.heliyon.2024.e35865

European Parliament. (2023). Artificial intelligence: Threats and opportunities. European Parliament. https://www.europarl.europa.eu/topics/en/article/20200918STO87404/artificial-intelligence-threats-and-opportunities

Fu, L. (2025). The role of STEM teachers’ emotional intelligence and psychological well-being in predicting their artificial intelligence literacy. Acta Psychologica, 253, 104708. https://doi.org/10.1016/j.actpsy.2025.104708

Gemini (chatbot) (2025, January 16). In Wikipedia. https://en.wikipedia.org/wiki/Gemini_(chatbot)

Gnambs, T., Stein, J.-P., Appel, M., Griese, F., & Zinn, S. (2025). An economical measure of attitudes towards artificial intelligence in work, healthcare, and education (ATTARI-WHE). Computers in Human Behavior: Artificial Humans, 3, 100106. https://doi.org/10.1016/j.chbah.2024.100106

Gura, D., Dong, B., Mehiar, D., & Said, N. A. (2024). Customized convolutional neural network for accurate detection of deep fake images in video collections. Computers, Materials and Continua, 79(2), 1995-2014. https://doi.org/10.32604/cmc.2024.048238

Hambrock, H., de Villiers, F., Rusman, E., MacCallum, K., and Arrifin, S. A., (2020). Seamless Learning in Higher Education: Perspectives of International Educators on its Curriculum and Implementation Potential (Rob Power, Editor). [eBook]. International Association for Mobile Learning. ISBN: 978-1-7751408-1-8. https://seamlesslearning.pressbooks.com/

Jaffri, A. (2024, November 11). Explore Beyond GenAI on the 2024 Hype Cycle for Artificial Intelligence. [Web log post]. Gartner. https://www.gartner.com/en/articles/hype-cycle-for-artificial-intelligence

Jasper (2025). The AI your marketing deserves. [Software application]. https://www.jasper.ai/

Khan, F., Bartáková, G. P., Almadhor, A., Qayyum, A., Abeer, K., Durrani, A. (2025). Evaluating the capacity and limitations of generative AI in financial decision making. Computer Standards & Interfaces, 93, 103965. https://doi.org/10.1016/j.csi.2024.103965

Kim, D., & Wang, Z. (2024). Social media influencer vs. virtual influencer: The mediating role of source credibility and authenticity in advertising effectiveness within AI influencer marketing. Computers in Human Behavior: Artificial Humans, 2(2), 100100. https://doi.org/10.1016/j.chbah.2024.100100

Klijn, F., Mdaghri Alaoui, M., & Vorsatz, M. (2025). Online academic exams: Does multiplicity of exam versions mitigate cheating? International Review of Economics Education, 48, 100305. https://doi.org/10.1016/j.iree.2024.100305

Kostopolus, E. (2025). Student use of generative AI as a composing process supplement: Concerns for intellectual property and academic honesty. Computers and Composition, 75, 102894. https://doi.org/10.1016/j.compcom.2024.102894

Lasisi, M., & Tembe, U. (2025). Digitization and intellectual property rights. In D. Baker & L. Ellis (Eds.), Encyclopedia of libraries, librarianship, and information science (1st ed., pp. 140-146). Academic Press. ISBN 9780323956901

Lee, V. R., Pope, D., Miles, S., & Zárate, R. C. (2024). Cheating in the age of generative AI: A high school survey study of cheating behaviors before and after the release of ChatGPT. Computers and Education: Artificial Intelligence, 7, 100253. https://doi.org/10.1016/j.caeai.2024.100253

Looi, C. K., So, H. J., Chen, W., Zhang, B., Wong, L. H., & Seow, P. (2012). Seamless learning. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp. 251). Springer. https://doi.org/10.1007/978-1-4419-1428-6_251

Midjourney (n.d.). [Software application]. https://www.midjourney.com/home

OpenAI (n.d.). ChatGPT. [Software application]. https://chat.openai.com

Otter.AI (2025). The #1 AI Meeting Assistant. [Software application]. https://otter.ai/

Perplexity (n.d.). [Software application]. https://www.perplexity.ai/

Playfoot, D., Quigley, M., & Thomas, A. G. (2024). Hey ChatGPT, give me a title for a paper about degree apathy and student use of AI for assignment writing. The Internet and Higher Education, 62, 100950. https://doi.org/10.1016/j.iheduc.2024.100950

Sharma, S. K., AlEnizi, A., Kumar, M., Alfarraj, O., Alowaidi, M. (2024). Detection of real-time deep fakes and face forgery in video conferencing employing generative adversarial networks. Heliyon, 10(17), e37163. https://doi.org/10.1016/j.heliyon.2024.e37163

Suno (n.d.). Make a song about anything. [Software application]. https://suno.com/

Sweeney, S. (2023). Who wrote this? Essay mills and assessment – Considerations regarding contract cheating and AI in higher education. The International Journal of Management Education, 21(2), 100818. https://doi.org/10.1016/j.ijme.2023.100818

Thao, L. Q., Kien, D. T., Thuy, D. T. T., Thuy, L. T. M., Bach, N. C., Duc, T. T., Gia Bach, H., & Cuong, D. D. (2024). Designing a deep learning-based application for detecting fake online reviews. Engineering Applications of Artificial Intelligence, 134, 108708. https://doi.org/10.1016/j.engappai.2024.108708

Turnitin (2025). Empower students to do their best, original work. [Software application]. https://www.turnitin.ca/

Wang, C., Li, T., Lu, Z., Wang, Z., Alballa, T., Alhabeeb, S. A., Albely, M. S., & Khalifa, H. A. E. W. (2025). Application of artificial intelligence for feature engineering in education sector and learning science. Alexandria Engineering Journal, 110, 108-115. https://doi.org/10.1016/j.aej.2024.09.100

Xu, T., & Baghaei, S. (2025). Reshaping the future of sports with artificial intelligence: Challenges and opportunities in performance enhancement, fan engagement, and strategic decision-making. Engineering Applications of Artificial Intelligence, 142, 109912. https://doi.org/10.1016/j.engappai.2024.109912

Yim, I. H. Y. (2024). A critical review of teaching and learning artificial intelligence (AI) literacy: Developing an intelligence-based AI literacy framework for primary school education. Computers and Education: Artificial Intelligence, 7, 100319. https://doi.org/10.1016/j.caeai.2024.100319

Zheng, L., & Yang, Y. (2024). Research perspectives and trends in artificial intelligence-enhanced language education: A review. Heliyon, 10(19), e38617. https://doi.org/10.1016/j.heliyon.2024.e38617