1 Artificial intelligence (AI) has impacted every industry, including the education sector. In this position paper, we explore how human-centered AI (HCAI) can be integrated in programming activities in school rather than discussing the design of user interfaces of HCAI systems. Our main proposal is to integrate HCAI in educational practices to reduce teachers' workload by providing meaningful scaffolds to the learners, connecting technology and domain knowledge.
HCAI empowers developers to build and design AI systems that support human self-efficacy,
promote creativity, clarify, and distribute responsibility, and facilitate social participation [
It is important to highlight that at the heart of HCAI is to recognize that the way intelligent systems
solve problems, especially machine learning, is fundamentally alien to humans without computer
science knowledge [
In a sociocultural perspective on learning, learning is seen as context-bound, situated in
socialpractices, and mediated by symbolic and cultural artifacts. Hence, this approach emphasizes
participation in different social practices [
The empirical data presented in this article is derived from a design-based research project [
Thematic analysis was used to analyze the entire dataset and screen for emerging common topics.
Thematic analysis is a qualitative method for identifying and organizing patterns of meanings across a
dataset to enable the researcher to make sense of collective or shared meaning and experiences [
In this section, we will address the research questions: 1) How is programming integrated into learning school subjects? and 2) How can programming take advantage of HCAI to improve learning? We address the research questions by presenting two different data excerpts that show how pupils work when using programming for solving physics tasks assigned by the teacher. Presenting the empirical data extract address the first part of the research question exemplifying how programming is integrated into school subjects. This is useful to provide a realistic context of how these data can be extended to integrate HCAI as a scaffold to help the learner. However, in the table where the data is presented, we have added a column named “AI chatbot,” which is our design scenario, being an example of how HCAI can be integrated and connected to programming practices when using micro:bit in an educational context.
When sketching out a design scenario for how a micro:bit can be extended to include HCAI, we uncovered that there are at least two different yet relevant directions: 1) focusing on scaffolding the domain-specific knowledge learning process (in our example, physics) or 2) scaffolding learning programming and how to use the micro:bit (a third approach emphasizing collaboration is presented as direction for further work). We will present one design scenario from each to cover both directions, reflecting data extract 1 and data extract 2. Data extract 1 and 2 below derive from one physics class where the students use micro:bit for solving physics related tasks. The AI chatbot presented in the fourth column in the tables is not included per se in the study, as it is created as an add-on for suggesting a future design scenario for reflecting on how an AI chatbot can be integrated in an already existing technology used in school, the micro:bit. These data extracts derive from a research project where we developed and implemented technology rich interventions in several K-12 classrooms, consisting of pupils ranging 12-16 years old. We followed three classes (20 students in each class) over two years and videorecorded our observations in the classroom of the students when they were working in groups on using micro:bit for solving subject-specific tasks. The scenarios are based on the lessons we learned.
Table 1 below presents data extract 1 derived from a lesson in physics where four pupils are working together on a task given by the teacher on how to program and use a micro:bit to measure conductivity. The pupils are experiencing problems with the micro:bit and the code as the assembly is not working properly in connection with measuring conductivity. Two of the four pupils are discussing the problem that starts by one of them asking a question.
Are you having trouble with how to connect pin1 and pin2 to the micro:bit? Type yes, if you need help.
Are you still having a problem with connecting pin1 and pin2 to the micro:bit? 7 8
The main argument in this position paper is that integrating HCAI in education has a significant potential to reduce teachers’ workload. As shown above, we present two data extracts regarding how programming is practiced in schools in our country, and we extended our view of learning by adding a column describing a design scenario concerning how an AI chatbot can scaffold these learning processes. The AI chatbot integrated into the micro:bit can be interacted with in two ways: 1) requesting the chatbot directly in the chat window (top-down invocation) or 2) it infers the need for scaffolding based on the learner’s actions and what the learner says (bottom-up invocation). When the computer is connected to the micro:bit, the chatbot uses the microphone on the computer, listens to the conversations, and tries to suggest scaffolds connected to what the learners are talking about.
However, as we can read out from extract 1 (Table 1) and extract 2 (Table 2) the AI chatbot interacts
in a multi-user context, which impacts how the AI chatbot reacts. In extract 1 and 2 we have group
collaboration as a premise, meaning several students are working together to create the code, however,
there is only one student that interacts with the computer and creates the program. This implies that
there is only one student that directly interacts with the AI chatbot during this time. However, it is
important to reflect upon how one can design a chatbot that also takes the interaction among several
interacting pupils into consideration. Can a future design scenario be that the AI chatbot can take
questions from different pupils at the same time into consideration? It would be useful with an
interactive AI chatbot that also can handle social interaction in groups. Recent research on chatbots
shows that a role for a chatbot could be to encourage non-active pupils to be more active, engaging
them in the discussions with peers and with the chatbot, helping students to become better collaborators,
identified as an important 21st century skill [
Another interesting reflection around a future design issue with the AI chatbot is to examine how it can take advantage of context-awareness. A context aware chatbot must seek to understand and support the aims of the user. In our case, in extract 1 and 2, which is a programming context, it quite essential that the AI chatbot can be aware of this context to be able to support the pupils in their specific learning activity. This means that the AI chatbot should have some built in mechanism for adaptive learning that enable to learn over time through interaction pupils of different need and background knowledge. The IA chatbot we have profiled is a programming expert with the aim of scaffolding pupils in K-12/lower and upper secondary school when learning programming.
Summing up, the research contribution with this position paper is discussing a scaffolding scenario for using an integrated chatbot in the online micro:bit programming environment for pupils to learn together how to use a new technology in an educational context from the challenging position of relating the technology to domain knowledge (e.g., middle school physics as we have profiled here).
It is interesting to reflect on the impacts of HCAI on both learners and teachers. For instance, learners get faster and instant feedback, with a greater chance of receiving more accurate answers. There are several benefits of integrating HCAI into programming processes in schools, such as reducing teachers’ workload and enabling a more flexible and accessible teaching experience to students. However, one of the most important challenges with creating an AI chatbot is providing it with accurate context to enable effective interaction with the learners. As seen in previous research, scaffolding pupils is not easy due to many complex factors that may impact the learning process. In conclusion, the main findings in this paper are as follows: • • •
An AI chatbot can provide meaningful scaffolds to pupils when learning to program Exploring HCAI from a sociocultural perspective on learning leads to interesting aspects of how HCAI can be designed as scaffolds in an educational context, e.g., classrooms.
Integrating HCAI in the educational context can reduce teachers’ workload.
Future research regarding how an AI chatbot could be designed and implemented into the micro:bit environment is warranted. Integrating HCAI in education, especially as an approach to “learning with AI,” has great potential to reduce teachers’ workload in classroom settings.