Artificial intelligence (AI) based on machine learning, neural networks, and large language models has created an enormous interest during the past year, peaking with the release of ChatGPT (GPT 3.5) at the end of 2022. The educational sector has been in turmoil as knowledge acquisition, effective teaching, and meaningful learning experiences are its foundational building blocks. In the following position paper, we explore how human-centered AI (HCAI) can be a useful perspective on AI in the age of generative AI (GAI) for the educational sector. However, we also suggest taking advantage of GAI tools to prepare the next generation of students for a future workplace requiring informed interaction with AI tools. We argue there is a lot of potential for applying AI in education, which can be advantageous for both teachers and students to increase the educational experience. However, there are also major challenges. For example, GAI tools do not yet align with learning theories that promote student agency during knowledge construction (e.g., constructivist learning theories).
Artificial intelligence (AI) as a tool has transformed many sectors of society, including healthcare,
finance, agriculture, and education. After the introduction of ChatGPT, AI as a concept has extended
its reach from technophiles to the public, ranging from skeptics to enthusiasts. There are several
definitions of AI with slightly different angles. One provided by ChatGPT is as follows: “Artificial
Intelligence (AI) refers to the ability of machines to perform tasks that would normally require human
intelligence, such as learning, problem-solving, decision-making, and natural language processing. AI
is achieved using algorithms and statistical models that enable machines to learn from data, recognize
patterns, and make predictions.” [
We argue in this position paper for the evolution of AI to HCAI by turning AI around to intelligence
augmentation (IA). Shneiderman [
Generative AI refers to a type of artificial intelligence that can create new content, such as images,
videos, music, and text, which is not based on pre-existing examples or data. GAI tools use different
types of algorithms to learn the underlying patterns and structure of the data, and generate new content
based on these structures. The algorithms focus on different aspects of the generative AI process such
as: transformer architecture, pre-training on large data sets, fine tuning for specific tasks, natural
language processing (NLP), and deep learning [
Generative AI can be used in a broad range of activities, for example in medicine and health care,
from creating synthetic medical images for training, to generating patient-specific treatment plans and
recommendations, to organizing administrative activities [
Our preliminary observations have highlighted a notable dilemma with the current generation of
GAI tools: while they excel in capturing intricate details, they fall short in embodying emotions and
meaning—both crucial human values [
Consider the production of literature references by GPT 3.5. At the lowest level, a reference is
composed of a sequence of words, which serves as the primary unit of analysis for the machine learning
algorithm. However, at a higher level, where human experts operate, the words are organized into
meaningful units, or aggregated components, referred to as intermediate building blocks such as
Authors, Title, Journal, and URL. Although GPT 3.5 may accurately reproduce each of these
components, the resulting artifact—a bibliographic entry—often turns out to be incorrect or
nonsensical, despite initially sounding plausible. This same dilemma is evident in images generated by
visual GAI tools like DALL-E. Each visual part of an image may be a flawless rendition of a specific
artwork piece, but when these parts are assembled within a broader context, they fail to convey
coherence or elicit an emotional response (Figure 1). Some proponents of AI tools argue that such
outcomes represent a unique creative aspect, while others view them as limitations of data-driven
machine learning. ChatGPT offers the following explanation: “Fixing this issue is challenging, as: (1)
during reinforcement learning (RL) training, there’s currently no source of truth; (2) training the model
to be more cautious causes it to decline questions that it can answer correctly; and (3) supervised
training misleads the model because the ideal answer depends on what the model knows, rather than
what the human demonstrator knows.” [
Shneiderman [
However, there are challenges and opportunities for K-12 education in implementing AI.
Akinwalere and Ivanov [
A critical role of HCAI with respect to GAI is to preserve context and make sure humans are kept in the loop during transformation, which is the process of turning sequential input data, such as natural language prompts, to generate output (text and images) based on machine learning models. This process is not in outset compatible with human learning theories, advocating agency, and social context, i.e., constructivist learning theories.
Constructivism is a theory originating more than 100 years ago that puts forward the hypothesis that knowledge is not passively received but actively built on an individual’s prior experiences [18]. It also considers the main function of cognition as adaptive to organize and make sense of the experiential world [19-21]. Social constructivism, a branch of constructivism, emphasizes social context and facilitation (instruction and scaffolding). This approach originated with Vygotsky [22] and is often referred to as sociocultural learning theory, which is the approach adopted in our research. Student agency is a central tenet when studying learning from a constructivist perspective, as it emphasizes that learners should have control over their own learning and be active participants in the construction of their own knowledge. Unfortunately, this feature is at odds with the current generation of GAI tools. The students are not able to control the knowledge construction process when interacting with these tools solely by input prompts, which is one of the main reasons for the current controversy in the education sector caused by ChatGPT and related tools.
Our statement in this position paper is that generative AI tools (GAI) (such as ChatGPT) provide numerous possibilities for enriching the educational sector both for teachers and students. However, some dilemmas exist that require further research: GAI tools provide detailed information about many topics, but it is not personalized to the student or teacher, which limits the learning experience. As mentioned in the previous section, the context generated by GAI tools are creative and explore new meanings rather than aiming to preserve original (e.g., historical, or cultural) meaning. The latter is more attuned to learning from a sociocultural perspective by emphasizing how a learning process always is situated in a social practice and contextualized in a cultural tradition. Therefore, in further research it could be interesting to explore how GAI tools can take the socio-cultural context of the learners more into consideration when interacting with the learner to generate new information. One scenario could be that instead of the GAI tools asking for textual input, the GAI tools could ask the learner to provide a description or picture of the context the learners have in mind to provide more meaningful and personalized output, which may enrich the learning situation for the parties involved.
Research questions from this perspective include: • How can future advancements in GAI tools incorporate the social context and learner background to enhance their functionality and effectiveness? • In what ways can the interaction with GAI tools be conceptualized as a contextualized learning process, fostering personalized dialogues that stimulate deep learning? • If the challenges in the first two RQs prove to be difficult for data-driven machine learning, what approaches can be employed to integrate "truth models" associated with specific domains of knowledge and expertise into GAI tools?
Another crucial avenue for further research lies in exploring the conceptual foundations of learning with GAI tools. Human learning is a multifaceted system characterized by various levels of abstraction and interactions between subsystems, and it necessitates careful consideration when it comes to interaction with GAI tools. Building upon prior work in HCAI, there is a need to examine the role of human interaction in complex learning systems involving AI tools, particularly in the education sector. This investigation can shed light on the concept of human-computer complementarity, determining the tasks at which computers excel and those that are best performed by human learners. Furthermore, it is worthwhile to reflect on how GAI tools can enhance user-adapted output by incorporating new sources of information. For instance, the GAI may request specific details from the user to customize the response, such as inquiring about their learning goals or the context in which the answer is required. By effectively leveraging this information and actively contributing to the construction of a more dynamic context, the GAI tool can be regarded as a partner in the interactional learning process. From this perspective, some research questions to explore include: • What are the optimal roles for computers and humans in the process of learning with GAI tools? • • • •
How can LLM GAI tools generate instructional feedback and personalize the learning experience? Should there be a word limit imposed on LLM GAI tools’ output to provide automated scaffolding of human generated text, rather than generating lengthy responses? How can the use of input prompts in successive steps facilitate the development of shared meaning (intersubjectivity) between humans and GAI tools, transcending their role as mere inputs to the AI system, while avoiding a sole focus on knowledge? How can GAI tools be effectively integrated into collaborative learning scenarios within virtual worlds and other online learning communities (e.g., metaverse), such as role-playing games and mass collaboration platforms?
[18] A. I. Mørch, V. Caruso, M. D. Hartley, End-user development and learning in Second Life: The evolving artifacts framework with application, in: F. Paternò, V. Wulf (Eds.), New Perspectives in End-User Development, Springer, Cham, 2017, pp. 333–358. [19] J. S. Bruner, The act of discovery. Harvard Educational Review 31 (1961) 21–32. [20] J. Piaget, The Origins of Intelligence in Children, International University Press, New York, NY, 1952. [21] E. Von Glasersfeld, Constructivism in education, in: T. Husen, T. N. Postlethwaite (Eds.), The
International Encyclopedia of Education Volume 1, Pergamon Press, Oxford, 1989, pp. 162–163. [22] L. Vygotsky, Mind in Society: The Development of Higher Psychological Processes, Harvard University Press, Cambridge, MA, 1978.