The Growing Conflict Between Copyright Law And The Evolving Landscape Of Digital Education And Modern Research

Author: LawSphereX

Category: Technology, Cyber & Privacy

Published: 2026-08-23T21:20:50.829233+00:00

Updated: 2026-08-26T17:05:44.853+00:00

THE GROWING CONFLICT BETWEEN COPYRIGHT LAW

AND THE EVOLVING LANDSCAPE OF DIGITAL

EDUCATION AND MODERN RESEARCH.

Adarsh M G, LLM, GLC Ernakulam Muhammed Feroz Sha, Advocate

ABSTRACT

This article critically examines the growing influence of artificial intelligence (AI) in academic environments, including education, research, and institutional service work. It explores how AI tools, particularly generative language models, are reshaping pedagogical practices, facilitating research activities, and streamlining administrative tasks. While these technologies present opportunities for innovation and efficiency, they also raise important concerns about academic integrity, research quality, equitable access, and the erosion of human-centric values in higher education. The article advocates for a balanced integration of AI that enhances academic processes without compromising mentorship, critical thinking, and institutional ethics.

Additionally, the article reflects on India’s approach to Digital Rights Management (DRM) in copyright law, questioning the relevance of restrictive provisions in a developing economy where broader access to educational material is crucial. Through this dual focus, the article highlights the need for thoughtful policy and pedagogical reforms that align technological advancements with the core mission of academic institutions. Keywords: Artificial Intelligence, Higher Education, Academic Integrity, Pedagogy, Generative AI, Research Quality, Academic Administration, Digital Rights Management, Indian Copyright Law, Technological Ethics.

Work and task automation have long been a subject of excitement and worry. Innovations such as washing machines, laptops, and mobile phones have found places in life so easily, while industrial robots have transformed industry by lowering costs and replacing workers. 1 Likewise, the fast-paced evolution of artificial intelligence (AI) is transforming industries, including the academic sphere. AI is increasingly utilized to augment and automate numerous activities, necessitating close interaction between technology and human beings. Higher education, especially fields affected by market influences and technological upsurges, is dramatically changing through AI adaptation. The conversation delves into the possibilities and challenges AI offers in classrooms.

AI and teaching Artificial intelligence tools like ChatGPT, Google Bard, and others have become very popular since their release late in 2022. Their increasing presence in educational settings has led teachers to rethink teaching methodologies and devise fresh methods of interacting with students. It has ignited controversies regarding the optimal way of incorporating AI into educational settings. Given that it is impractical to prohibit students from using AI, most educators have been pushing for positive incorporation into classrooms. 2 Education has several goals: broadening knowledge, critical thinking, and learning for a lifetime and not just memorization. AI can change the way people teach and learn and bring about a shift from instructors to facilitators, something already being seen in 'flipped classroom' structures.

As AI is capable of producing well-drafted answers in a fraction of a second at no cost, students can depend on these tools for synthesizing information. Therefore, universities need to place greater importance on the value of classroom participation, teamwork, and critical discussion. 3 Although AI can assist in many aspects of learning, it cannot substitute human empathy and one-on-one interaction. This highlights the growing need for mentorship and direct student interaction in higher education. The educator's role goes beyond lecturing to encouraging 1 Sadaf Fahim, “Artificial Intelligence vis-à-vis IPR Implications”, Purvi Pokhariyal, Amit K Kashyap, Arun, Artificial Intelligence Law and Policy Implications 128-142 (EBC, Lucknow, 1st edn. , 2020). 2 Fiona Macmillan, New Directions in Copyright Law, Volume V 72 (Birkbeck University of London, London, 2007).

3 Simon Stokes, Digital Copyright Law and Practice 23 (Butterworths Lexis Nexis, Edinburgh, 1st edn. , 2002).

meaningful discussion, facilitating intellectual inquiry, and providing individualized support. Teachers can use AI to optimize their pedagogical methods. AI content can help to organize course modules, generate ideas for classroom activities, and supply appropriate examples to discuss. Students, on their part, can use AI to research topics, question teachers, and regrettably, in some instances, plagiarize. 4 To meet this challenge, classrooms need to emphasize curiosity, analytical thinking, and problem-solving over passive acquisition of knowledge. AI’s ability to generate answers necessitates a pedagogical shift that focuses on inquiry, debate, and experiential learning. By fostering interactive and thought-provoking discussions, universities can ensure AI complements, rather than replaces, traditional teaching methods.

Beyond teaching, AI is streamlining administrative tasks such as lecture evaluations and workflow management. Automating these processes can enhance efficiency and reduce administrative burdens. AIbased tools also help teachers prepare lectures, organize syllabi, and create teaching materials, which makes it simpler to create interesting lesson plans. Additionally, AI has the potential to transform evaluation methods through providing customized assessment approaches tailored to specific learning requirements. AI can review students' patterns of engagement and levels of understanding, which would enable teachers to adjust their teaching techniques. AI-based evaluations potentially enable teachers to create specialized learning encounters that target the most difficult or interesting topics in a subject.

The growing application of AI in academia is accompanied by opportunities as well as challenges. While AI technologies enrich learning experiences and simplify administrative tasks, they also require a rethink of conventional pedagogical practices. Teachers need to adopt AI as an aid while preserving the essence of mentorship, critical thinking, and human engagement. As AI develops further, institutions need to reconcile technological progress with pedagogical integrity. The future of learning will be based on how well AI is incorporated to supplement human knowledge so that learning is dynamic, interactive, and relevant. 5 Research and AI Generative AI is on the cusp of revolutionizing the research environment, and it promises both difficulties and possibilities. Among the most serious issues is the mounting submission to

4 Ibid.

academic journals of poor-quality or useless manuscripts. The pressure on researchers to publish in top-tier journals has for a long time helped fuel an upsurge in submissions that are not original, well-organized, or soundly argued. Some articles are poorly developed, demonstrating little effort on the part of authors who value putting publications on their CVs more than actual academic contribution. Others are sent to journals without consideration of their scope or research interest, resulting in a high rate of desk rejections by editors. With the introduction of AI writing tools like ChatGPT, the simplicity of manuscript production will only worsen these problems, further burdening editors, peer reviewers, and institutions. These groups already spend a considerable amount of time and effort screening out low-quality submissions, frequently with little visibility or reimbursement.

With more AI-generated submissions, journals must come up with new ways of maintaining research quality and authenticity. In spite of these issues, generative AI can also introduce more equity into scholarly authorship. Honorary co-authorship, one of the long-standing problems in academia, is when senior scholars are listed as co-authors on a paper with little contribution, only because they have made superficial language edits but are given authorship credit because of their institutional status or native English proficiency. With AI tools offering advanced capabilities to enhance writing quality, the justification for such honorary authorships may diminish. AI can assist nonnative English speakers in articulating their ideas more clearly and effectively, reducing reliance on senior scholars for linguistic refinement.

While AI itself cannot assume authorship given its lack of accountability and intellectual contribution it can help democratize academic publishing by allowing researchers to present their work more independently. Apart from writing support, AI is also revolutionizing data gathering and analysis. AI tools are capable of analyzing large sets of text, recognizing patterns, and creating insights that supplement conventional research techniques. In qualitative studies, AI is already part of software used in text mining and thematic analysis. In survey studies, AI is capable of creating dynamic followup questions based on the responses of participants, increasing the depth of data gathering. As research methods are changing, human understanding and the use of AI-based analysis will generate new grounds for knowledge generation.

The increasing role of AI in research requires a deliberate approach to its incorporation. While it can improve efficiency and accessibility, it also introduces ethical and quality issues that are to be considered by academic institutions and publishers. The difficulty rests in harnessing the strengths of AI while preserving rigor, novelty, and ethical standards of scholarship.

Academic service and AI Modern language models like ChatGPT provide useful prospects for optimizing serviceconnected academic work, especially in the context of an internationalized educational system where the first language may not be English for many practitioners. Composition in a second language can be a time-consuming and uncertain process to ensure that what is produced corresponds to the imposed style, lexicon, and readability. It can even pose difficulties for mother-tongue speakers to explain things clearly, so text optimization is an issue regardless of language background. Scholars need to create a range of written works for various types of audiences and usually in specific genre conventions. These range not just to research papers but to administrative and service documents as well.

The structured character of a lot of scholarly works implies that their creation also follows routine conventions, which could be automated or perfected by AI. A notable case in point is in drafting reference letters for applicants for work or promotions,

including students and colleagues. They are a crucial academic service but very seldom counted as a part of official workload assignments. While certain academics can readily summarize a candidate's qualifications and merits, others might consider it sluggish and challenging. AI can be used to come up with a well-structured and properly toned draft, which the author can then make personalized with concrete examples. This application of AI is similar to the prevalent academic practice of recycling already written letters to a new end. Instead of displacing human intervention, AI may act as a facilitator to enhance efficiency, enabling academics to concentrate on substance over time spent on writing routine documents.

As AI applications in academic writing become more sophisticated, its contribution to aiding both research and service-type activities will extend further, enabling more effective communication and a decrease in administrative loads. 6 AI can be likened to a calculator, where the human writer is still accountable for the end result while the machine helps in completing crucial steps. Similar to the use of a calculator, which is problematic when manual arithmetic is needed, AI's application in academic work can be criticized when original thought and independent effort are necessary. But where efficiency and precision are more important than the process, AI, as a calculator, is an accepted and standard tool. AI's utility goes beyond writing support to administrative tasks like updating 6 Simon Stokes, Digital Copyright Law and Practice 24 (Butterworths Lexis Nexis, Edinburgh, 1st edn.

, 2002).

internal policy reports, preparing accreditation-related documentation, and drafting work emails. Contemporary email software already integrates AI suggestions, subtly steering users toward formulating their messages. In this respect, AI is already integrated into academic service work, and its potential for further integration is immense. AI's application in academic service work, though, has issues. Like its effect on education, AI can do many administrative tasks more efficiently than humans. Rather than just taking workload off and freeing up time for creative and complex work, AI could result in redundancies, putting more pressure on remaining academic and administrative staff. In addition, it could reduce the value of collaborative debate that informs institutional policy.

The process of jointly debating, interrogating, and editing policy documents promotes transparency, organizational integrity, and institutional culture. If this process is replaced by AI-generated documents, universities risk losing valuable opportunities for constructive debate. Historians have been warning of the risks of technological advancement for its own sake, without regard to human needs. Universities are in place exactly to address these needs. Their role in society goes beyond producing knowledge and educating students, as they play central roles in their immediate communities and economies, supporting good governance and socio-economic development. They are also significant employers who work towards regional stability through supporting employment levels.

Keeping the human-oriented purpose of academia in line with AI's efficiencies is paramount in ensuring that technological innovation serves and does not undermine the founding values of educational institutions. 7 We should be careful of the potential risk that AI in service work might erode the broader societal role of universities. Historians have argued for decades that, despite its neutrality in not being purely good or evil, technology is not neutral at all. It actually modifies lifestyles, values, and institutions. It is only if we accept this argument thoroughly that we would need to examine the possible effect of AI on the values and institutional functions assigned to educational establishments.

The growing dependency on AI in university service work could lead to a change of priorities, where cost and time efficiency become overarching concerns at the expense of good governance, employment generation, and equitable working conditions. Universities and business schools need not only be optimized organizations of streamlined productivity but also remain institutions that support sustainable livelihoods, academic

7 7 Ibid.

honesty, and long-term social development. Rather than uncritically embracing AI for administrative efficiency, institutions should ensure that technological integration aligns with their fundamental mission. A careful balance must be struck, where AI is used as a supportive tool rather than a force that erodes the human-centered values at the core of academic institutions.8

CONCLUSION

The inclusion of DRM provisions in Indian copyright law has posed a number of important questions, such as whether a developing economy needs enhanced copyright protection. Without DRM provisions, digital copyright material would be freely accessible for educational use under fair use exceptions, and consumers would have full rights over digital content purchased by them, including the right to transfer ownership under the first sale doctrine. Adoption of DRM provisions in advanced economies has re-oriented the copyright law equilibrium in the favor of copyright holders against end users. Indian law, on the other hand, is less restrictive compared to US and EU laws since it does not have stringent access control provisions, manufacturer liability, and an intent test for determining liability. Still, Indian DRM provisions do pose access difficulties.

They presume that individuals who are exempt from liability, such as the differently-abled, would have the capability of circumventing technical limitations which does not necessarily occur. Secondly, the duty of keeping proper records of acts of circumvention unnecessarily places an added burden where it is unclear in legislation what has been expressed in this context. While softer than Western versions, India's DRM legislation is potentially unnecessary because India would reap more benefits from wider access to copyrighted material. Aggressive anticircumvention provisions have the potential to limit such access, possibly preventing education, innovation, and digital inclusion in a developing economy.

8 Available at: sagepub.com (last visited on January 03, 2024).