Introduction

As AI became available to the wider public the academic environment became aware of its potential for education. The concern that AI would be used without guidelines or policy questions raises the question how it could be implemented to improve a seamless learning experience for students. This study addresses the concern by firstly presenting the development of AI functionalities for a better understanding thereof and continues to present the seamless learning framework. Thereafter, the affordances of AI in an educational context for students and educators are discussed and ends with suggestion on how  AI can be used to optimize a seamless learning experience for students.

A Historic Overview of the Development of AI

Artificial Intelligence has been available for business and academic environments for over seven decades by starting with the development of Large Language Models known as NLP’s (Natural Language Processing). During the 1950’s a translation program was developed when IBM programmers and linguists from Georgetown University wrote the first algorithms to connect numbers with language patterns for translating words from Russian into English and vice versa. The computer was first programmed to recognize single words and then to translate them and eventually organizing them into meaningful sentences (Norman, 2023).

Another example of “teaching” a computer to communicate with a human being was piloted in 1956 when the Los Alamos Scientific Laboratory conducted a study where a computer was programmed to play chess against a human being. MANIAC, became the first computer to defeat a human in a chess-like game. Playing with the simplified Los Alamos rules, it defeated a novice in 23 moves. In 1957 IBM engineer and mathematician Alex Bernstein wrote the first complete computer chess program in history, which ran on an IBM 704. It could process 42,000 instructions per second and had a memory of 70 kilobytes (IBM, 2024).

Over time more algorithms were written, and information was “fed” into the internet creating a mega library consisting of codes which could be found when search engines also known as web crawlers were used by typing in specific words. For example, the Google search engine was one of these web crawlers that was used to find information on the www world wide web (Bergman, 2001).

Additionally, different types of Artificial Intelligence were created known as AGI, Ani and ASI. ANI, solves specific problems within its domain. AGI can tackle a wide range of problems, often with human-like reasoning, and ASI solves complex problems efficiently, potentially discovering novel solutions beyond human comprehension. The value of these tools was only known by computer programmers but not the wider public (Ediweekly, 2024).  Furthermore, chatbots such as Alexa and Siri were introduced by Apple (2025) and Amazon (2024). They are communicative AI tools that search for information on the internet and verbally provide the information in a kind and ethically correct voice (CFTE, 2021). Besides the AI information database.  AI-based tools also included the development of GPS by collecting map information and offering driving assistance. (Tak, et.al 2021).

Some of these tools were known by users with access to the internet, but not everyone knew how to use them (Chukwunonso, 2013). Until a new form of Artificial Intelligence was made available to the public in November 2020: The Chatbot Generative Pre-Trained Transformer known as ChatGPT which can search and find information on requests from a human by accessing a huge database and not only finding and providing existing information but having human-like capabilities of contemplating, comparing and giving its own opinion on a topic (Dwivedi et al., 2023).

At first, ChatGPT was used by a handful of users but over the past two years it spread through the world like a wildfire. Similar GenAI tools have been developed and are currently being used for a multitude of tasks. These include writing reports, setting tests, writing curriculum and even writing speeches for the president of a country (Alexander, 2024)

With this information in mind, the study continues to discuss the affordances and challenges AI technologies provide to students and educators (Owoc, et.al., 2019).

An Overview of the Seamless Learning Experience Design Framework

The Seamless Learning Experience Design (SLED) freamework (Hambrock & De Villiers, 2023) includes five important concepts that are needed for a successful learning experience for the student. These concepts are presented below.

The core concepts include selecting teaching methodologies and pedagogies that are appropriate for an optimized student learning experience.

The positive concepts include adding motivational concepts that contribute to the student’s successful seamless learning experiences to reach his/her goal.

The practical concepts include availability of technology and infrastructure in all its possible forms,

The human concepts include the skills a student needs to improve his or her learning.

The design concept includes the selection of instructional design approaches to improve transitioning from one learning environment to another.

In terms of using AI to improve the five concepts of designing a seamless learning experience each one of the core concepts can benefit from AI as support when needed.

Affordances of AI Tools – Advantages and Disadvantages

Question 1: What are the Advantages of AI for Students?

The advantages for students using AI tools include the following:

• Improves searching for information on the Internet. AI gives students quick and easy access to information.
• Improves support- AI provides support to students when they are asking questions to clarify certain topics.
• Facilitates accessibility- AI facilitates student access to high-quality educational resources, regardless of economic status or geographic location (CIS, 2023).
• Enables personalized learning– AI enables personalized learning by tailoring educational content to meet the unique needs of each student.
• Enables adaptive learning -Through adaptive learning technologies, artificial intelligence can analyse a student’s strengths, weaknesses, learning pace and preferences. This data allows AI systems to provide customized lesson plans and resources, ensuring that students receive instruction that is best suited to their individual learning preference.
• Enables self-paced learning. AI can prepare personalized study material for students so they can progress at their own pace, which helps to improve understanding and retention of material (Clugston, 2024).
• Enables immersive learning experiences – AI has the potential to create immersive learning experiences that engage students in ways traditional methods cannot. Real-world scenarios and complex concepts can be accessed by Virtual or Augmented Reality simulations, making learning more interactive and enjoyable. (Clugston, 2024).
• Improves student engagement and motivation – AI creating immersive learning experiences also can result in boosting student engagement and motivation by making learning more interactive and personalized. Gamified learning platforms, powered by AI, incorporate game elements such as rewards, challenges and leaderboards to make learning fun and competitive.
• Improves feedback and support- AI can provide instant feedback and support, helping students stay motivated and focused on their learning goals. By addressing individual needs and offering real-time assistance, AI helps maintain student interest and encourages active participation (Clugston, 2024).
• Provides integrated learning and intelligent tutoring system – AI-driven intelligent tutoring systems offer personalized guidance and support to students, mimicking one-on-one tutoring. These systems use data analytics to understand a student’s learning progress and provide targeted feedback and recommendations. They can identify knowledge gaps, suggest relevant resources and adjust the difficulty level of tasks to match the student’s abilities. By offering tailored support and continuous assessment, artificial intelligence helps students achieve their learning objectives more effectively (Clugston, 2024).

Question 2: What are the Disadvantages of AI for Students?

To achieve a successful learning experience all students need access to AI tools. Unfortunately, not all students have access and overarching bridge need to be fixed. Not all students are in the correct environment with the correct data speed, using AI as a tool to find information can be useful but only makes sense if accessibility is provided to all students in all educational institutions. The implication of limited access will rather increase the knowledge divide and needs to be addressed by governments very urgently.

Other disadvantages that AI brings include:

• Increased technological dependency – With the integration of AI, there is a risk that both educators and students will become too dependent on technology, affecting the development of what we know today as power skills.
• Compromised privacy issues – When using AI-based platforms, student data may be at risk if not properly managed.
• Increased depersonalization – While AI can personalize learning, it can also cause the educational process to become mechanized and unnatural.
• Decreased memory and retention of knowledge- Previously, students were forced to memorize all types of data such as historical dates, authors, philosophical currents… etc. The Internet has made knowledge ubiquitous and easily accessible, therefore the ability and habit of memorization has gone decreasing as the Internet advanced. This process of “collective forgetting” will increase exponentially with AI (CIS, 2023).
• Dependence On Technology – Another major concern is the growing dependence on technology that AI in education fosters. As educational institutions increasingly rely on AI-driven tools for teaching, assessment and administrative tasks, there is a risk of becoming overly dependent on these technologies. This dependence can lead to significant disruptions in the event of technical failures or cyber-attacks. Furthermore, it may also diminish the development of critical thinking and problem-solving skills among students, as they may become accustomed to AI systems providing answers and solutions.
• Lack of Human Touch/Dehumanized Learning Experience – The lack of human touch is a critical disadvantage of AI in education, leading to a dehumanized learning experience. Traditional education relies heavily on human interaction, with teachers providing not only academic instruction but also emotional support and mentorship. AI systems, while efficient, cannot replicate the empathy, understanding and personal connection that human educators offer. This absence of human elements can affect students’ social and emotional development, as well as their overall engagement and motivation in the learning process.
• Risk Of Cheating – AI in education also raises the risk of cheating. Advanced AI tools can be exploited by students to find ways to bypass academic integrity measures. For instance, AI-powered plagiarism detection systems may themselves be outsmarted by sophisticated AI-generated content that mimics genuine student work. Additionally, AI-based tutoring systems and automated assessments might be manipulated to provide undue assistance, undermining the fairness and integrity of academic evaluations. Schools and institutions must continuously adapt and update their AI tools to mitigate these risks.

This means that its up to the user how responsibly AI is applied, as it can be used or misused. A guideline for usage of AI needs to be provided to students so they know the boundaries and the advantages as well as the disadvantages.

Advantages of AI for Education as a Whole?

Besides offering students a personalized experience the affordances of AI for education as a whole are also impressive. These include the following:

• AI offers cost-effective learning- AI can make education more cost-effective by automating administrative tasks and providing scalable learning solutions. For example, artificial intelligence can handle routine tasks such as grading assignments, scheduling and managing student records, freeing up time for educators to focus on teaching. Additionally, AI-powered educational platforms can reach a large number of students with minimal additional cost, making high-quality education accessible to a broader audience. This scalability helps reduce the overall cost of education and ensures that more students can benefit from quality learning experiences (Clugston, 2024).
• Increased effective follow-up- Lecturers and students can use AI directly from the phone. Educators can access and grade students work on the go.
• Administrative efficiency – Educational institutions can use AI to automate administrative tasks, allowing educators to focus more on teaching and less on bureaucracy.
• Continuous assessment – AI can assess each student’s progress and provide real-time feedback, helping them identify their strengths and areas for improvement.
• Continuous evaluation and improvement in the long run –AI facilitates continuous evaluation and improvement by providing real-time insights into student learning performance and learning outcomes. Through data analytics, AI can track student progress, identify trends and highlight areas for improvement. Educators can use this information to refine teaching strategies, develop personalized interventions and ensure that learning objectives are being met. Continuous evaluation enables a proactive approach to education, where adjustments can be made promptly to enhance the learning experience and outcomes (Clugston, 2024).
• Raising academic standards and educational quality – The integration of AI in education has the potential to raise academic standards and improve the overall quality of education. Artificial intelligence can help ensure consistency and accuracy in grading, provide access to high-quality resources and support educators in delivering effective instruction. By leveraging AI technologies, educational institutions can offer a more rigorous and comprehensive curriculum that meets the evolving needs of students. AI can facilitate collaborative learning environments, where students and educators can share knowledge and resources, fostering a culture of continuous learning and improvement.

The advantages of AI in education are manifold, ranging from personalized and immersive learning experiences to cost-effective and high-quality education. As AI continues to evolve, its role in transforming education will become increasingly significant, offering new opportunities for students and educators alike (Clugston, 2024).

Disadvantages for Education as a Whole

The main disadvantages for education include the following:

Data Privacy Concerns – One of the primary disadvantages of AI in education is the issue of data privacy. AI systems often require vast amounts of personal data to function effectively, including students’ academic records, behavioural data and even biometric information. This extensive data collection raises significant concerns about how this information is stored, used and protected. Inadequate safeguards can lead to data breaches, exposing sensitive student information to unauthorized parties and potentially resulting in identity theft or other forms of misuse. Ensuring robust data privacy measures and compliance with regulations is key to protecting students’ information.

Over dependence on AI – To become too dependent on AI can have a negative impact on the students’ understanding and development of their brain. This needs to be regulated by the students and checked by the tutors and lecturers. This also means that students cannot be left to educate themselves on their own. They still need their lecturers to support and guide them along.

Suggestions and Conclusion of AI for a Seamless Learning Experience

Considering the above advantages and disadvantages of AI it is crucial to focus on what educators want to achieve and what students really need to learn. The tool can become a distractor and instead of promoting the learning process the student can be distracted and immersed into the many opportunities AI offers. In order to achieve a smooth and seamless self-paced learning experience environment AI should be used as a mindtool. It should not be used for the sake of technology but for the sake of improving learning.

In order to optimize a seamless learning experience the following suggestions are important for consideration:

Train educators – Teachers must be equipped with the skills and knowledge necessary to effectively use AI in education. This includes understanding the tools available and how to adapt them to the needs of their students.

Balanced use of AI – Finding a balance between traditional teaching and AI-based tools is critical to ensure successful implementation thereof. Technology should complement, not substitute, human interactions in the classroom.

Focus on ethics – Ethics must be a primary consideration during the implementation of AI in education. Educational institutions must ensure that student data is protected and that the technology is used responsibly.

Continual revisions – Technology and its applications are constantly evolving. Institutions must be willing to adapt and change, regularly reviewing how AI is used and whether it meets their stated educational goals. This ongoing review process is called by academics “Keeping the Ax Sharp”.

In conclusion, the challenge of using AI tools for the improvement of students’ seamless learning experience is directly connected with the knowledge of what the SLED concepts include and how the AI tools can support the improvement of the concepts as well as what educators need to ultimately improve students’ performances.  The concept of Jonassen (1996) using technology and in this case AI tools have a place in education but should be used as “mind tool” and nothing more. This approach together with the principles of the seamless learning framework can optimize the learning experience of the students to achieve positive results and preparing them for a career in the future.

References

Alexander, B. (2024) Two years after the ChatGPT explosion. [Web log post]. AI, Academia, and the Future. https://aiandacademia.substack.com/p/two-years-after-the-chatgpt-explosion

Amazon Developer (2024). Alexa: Build LLM-powered Alexa experiences. https://developer.amazon.com/en-US/alexa/alexa-ai

Apple (2025). Siri. https://www.apple.com/ca/siri/

Bergman, M. K. (2001). White paper: the deep web: surfacing hidden value. Journal of electronic publishing, 7(1).

CFTE (2021, February 11). Conversational AI Examples: How Siri, Alexa & Google Assistant have Human-like Conversations. [Web log post]. The Centre for Finance, Technology & Entrepreneurship. https://blog.cfte.education/conversational-ai-examples-how-siri-alexa-google-assistant-have-human-like-conversations/

Chukwunonso, F., Ibrahim, R. B., Selamat, A. B., Idama, A., & Gadzama, W. A. (2013). The impact of the internet and the world wide web on distance and collaborative learning. ICCGI, 7-15.

CIS (2023, October 23) The Benefits of AI in Education. https://www.cis-spain.com/en/blog/the-benefits-of-ai-in-education/

Clugston, B. (2024, July 19) Advantages and disadvantages of AI in education. [Web log post]. University Canada West. https://www.ucanwest.ca/blog/education-careers-tips/advantages-and-disadvantages-of-ai-in-education/

Ediweekly (2024). The three different types of artificial intelligence. https://www.ediweekly.com/the-three-different-types-of-artificial-intelligence-ani-agi-and-asi/

Foote, K. (2023, December 28). A Brief History of Large Language Models. [Web log post]. Dataversity. https://www.dataversity.net/a-brief-history-of-large-language-models/

Hambrock, H., & De Villiers, F. (2023). Proposing a seamless learning experience design (SLED) framework based on international perspectives of educators from five higher education institutions. Electronic Journal of e-Learning, 21(1), 52-68.

IBM (2024) History Deep Blue. https://www.ibm.com/history/deep-blue

Jonassen, D. H. (1996). Computers in the Classroom: Mindtools for Critical Thinking. Prentice-Hall, Inc.

Norman, J. (2023) The First Public Demonstration of Machine Translation Occurs. HistoryofInformation.com. https://www.historyofinformation.com/detail.php?id=666

Owoc, M. L., Sawicka, A., & Weichbroth, P. (2019, August). Artificial intelligence technologies in education: benefits, challenges and strategies of implementation. In IFIP International Workshop on Artificial Intelligence for Knowledge Management (pp. 37-58). Springer International Publishing.

Tak, S., Lee, J. D., Song, J., & Kim, S. (2021). Development of AI‐Based Vehicle Detection and Tracking System for C‐ITS Application. Journal of advanced transportation, 2021(1), 4438861.