# What Type of Leaf is It? – AI in Primary Social and Science Education

Stephan Napierala, Jan Grey, Torsten Brinda, Inga Gryl

► To cite this version:

Stephan Napierala, Jan Grey, Torsten Brinda, Inga Gryl. What Type of Leaf is It? – AI in Primary Social and Science Education. IFIP World Conference on Computers in Education (WCCE), Aug 2022, Hiroshima, Japan. pp.233-243, 10.1007/978-3-031-43393-1_22. hal-05533309

HAL Id: hal-05533309

https://inria.hal.science/hal-05533309v1

Submitted on 2 Mar 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
<figure><img src="image_2.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
# What Type of Leaf is It? – AI in Primary Social and Science Education

Stephan Napierala<fnref n="1" />(✉) ID, Jan Grey<fnref n="2" /> ID, Torsten Brinda<fnref n="1" /> ID, and Inga Gryl<fnref n="2" /> ID

{stephan.napierala, torsten.brinda}@uni-due.de

{jan-grey, inga.gryl}@uni-due.de

**Abstract.** Digitization is a crucial process that is transforming our modern lives. As such, it is essential for students to be able to act maturely in a digital world and to participate in society responsibly, based on their education [1]. This is where digital literacy comes in as a key competence for both: social participation and self-determination in a digital world. To gain a deeper understanding of the digital technologies that surround us, computing education is a valuable way to go. It can help to explain the underlying principles and phenomena of the digital world. Many countries have begun incorporating it into their primary school curriculum. In Germany, and in particular in its federal state North Rhine-Westphalia, computing education is embedded in the Primary Social and Science Education (PSE, in German Sachunterricht). However, there is currently a lack of comprehensive training and education concepts for (future) teachers and corresponding teaching materials to include computing education in this subject, aside from research projects. To support the integration of computing education into PSE, we have developed teaching materials on Artificial Intelligence (AI) using identification apps. These materials provide students with insights into how AI systems perform classification tasks, such as determining different types of leaves, and cover basic concepts of Machine Learning (ML). The materials have been tested in one primary school class and validated by student teachers during their teaching internship semester. This paper describes the action-oriented materials and first classroom experiences.

**Keywords:** artificial intelligence · machine learning · primary education

## 1 Introduction

The increasing digitization is changing the lives of children and young people [2], and it poses new challenges for socialisation and school education in our society. In particular in primary schools, the requirements of the digital world must be

<footnote n="1">¹ Computing Education Research Group, University of Duisburg-Essen, Essen, Germany</footnote>

<footnote n="2">² Institut for Primary Social and Science Education, University of Duisburg-Essen, Essen, Germany</footnote>
addressed as early as in the school entry phase, so that students are empowered to act responsibly within their environment [3].

In Germany, the subject of Primary Social and Science Education (PSE) is focused on the students’ experience realm, addressing phenomena from the living environment and using multi-perspective approaches. With regard to the digitized world, teaching *with* digital technologies as well as teaching *about* principles of digital technologies is subject of primary social and science education [4,5]. Principles that already play a major role in our and students’ daily lives are Artificial Intelligence (AI) and Machine Learning (ML). They are used to make life more comfortable and appear, e.g., in voice assistants (such as Siri or Alexa), search engines on the internet, navigation systems, facial recognition software for unlocking smartphones or recommendations for streaming services. Students naturally interact with such systems without perceiving their presence or knowing anything about their functional principles. Taking up such objects and phenomena is part of a comprehensive digital education that should be delivered as early as possible, as outlined by the CECE: “All students must have access to ongoing education in computer science in the school system. Computing education should preferably begin in primary school, at the latest at the beginning of secondary school” [6].

To address this need, the project “Computing Education as a Perspective of Primary Social and Science Education in Teaching Internships during Teaching Degree Study Programs” aims to answer the question: How can prospective teachers be enabled to provide computing education in primary schools during their practical phase of their studies? At the Institute for Primary Social and Science Education at the University of Duisburg-Essen, university teacher training was redesigned to allow students to acquire scientific and educational concepts in computing and test teaching materials about AI and ML in practice, both before and during their teaching internships at school [7]. Developing appropriate teaching materials is crucial for all kinds of schools, but particularly for primary schools, where computing education is typically provided by generalist teachers who may not have specific training in computer science. Therefore, a broader support is required for both prospective and active teachers to acquire the necessary experiences, skills, interest, and self-confidence [8] to incorporate computing education into their teaching.

In our project, we pursue a two-part, future-oriented approach: On the one hand, we have embedded computing education into the PSE course at the university for students who will become primary school teachers. On the other hand, we have developed material for primary school teaching, in order to make low-threshold offers to active teachers in schools. This approach allows us to reach both future and active teachers, and to raise awareness about computing education in PSE. We have chosen this approach, because adequate training of both pre-service and in-service teachers is essential for successfully embedding computing education [9].

In this paper, we focus on supporting active teachers by presenting our developed material on AI and ML. Therefore, we first take a look at the curricular
integration of computing education into primary education in Germany and especially AI and ML in Primary Education (Sect. 2). Afterwards we describe the development of the material in Sect. 3, practical experiences are presented in Sect. 4.

# 2 Related Work

## 2.1 Objectives and Curricular Embedding of Computing Education in Primary Social and Science Education in Germany

As previously mentioned, in Germany, computing education is not a standalone subject in primary school. Instead, it is integrated into the subject of Primary Social and Science Education (PSE), with the goal of promoting students' ability to act and empower them to participate in a digitally-shaped world and society [10]. This “digital” education, which is a central goal of school education in general [11], includes both using teaching and learning *with* and *about* digital media and technologies. The multi-perspective approach of PSE provides the opportunity to examine topics from both: a social science and natural science perspective. In relation to the digitally-shaped world, computing principles represent a perspective for PSE to engage with digital media. As a result, digital education and especially computing education are integrated into the PSE curriculum. The current curriculum for the state of North Rhine-Westphalia covers the principles of data processing (input-process-output principle), algorithmics, and programming [12]. The *German Informatics Society (GI)* provides an important framework, with recommendations for computing education in lower [13], upper secondary education [14] and primary education [15].

Incorporating computing education into PSE can help to address digitization-related (mis-)conceptions and to reflect on digital technologies. Since the curricular establishment is still comparatively new, it is not possible to assume a broad implementation in practice, yet. Reasons for this include inadequate concepts and materials, lack of knowledge and experience of teachers regarding teaching about digital media [9,16] and subjective competence assessment of teachers [17]. To address these challenges, the project presented here integrates computing education into university teacher training at three universities in North Rhine-Westphalia and develops, among other things, material in the fields of AI and ML. Although AI and ML are not explicitly stated in the primary school curriculum, they are an opportunity to understand how a variety of commonly used computing systems work. They can also be used to reflect on underlying value systems and to sensitize students to the possibilities, limitations, and risks of different computing systems. As AI and ML are increasingly present in students' everyday lives and are becoming increasingly important, they are relevant topics for school education [18,19]. Teachers should be able to educate students about the principles and phenomena of computing systems. By incorporating computing education into teacher education, we offer students the opportunity to develop basic computing education skills, which will enable them to create and carry out lessons about computing principles such as AI and ML [20].
## 2.2 AI and ML Material

Internationally, there are already several successful materials and approaches for teaching primary school students basic computing principles in the fields of AI and ML. The PopBots project [21] developed an AI curriculum that uses LEGO robots, which can be programmed in a block-based way to explore AI with young children. “Learn to Machine Learn” (LearnML) is another project in which an AI education framework and game-based material were developed for younger and older students which has already been used in teacher education [22]. The Teachable Machine<fnref n="1" /> by Google is another project, which has already been successfully used with primary school students to teach them basic principles of ML [23].

However, all of these projects have a fundamental structural problem: they are only available in English and are not ready for use in other countries, as primary school students usually do not have a comprehensive knowledge of English. The material must therefore be translated into their native language first, which is not always possible, as some projects use specific software or websites. Therefore, several material can not be used in German-speaking primary schools. This lack of suitable material (in the national language) is a major obstacle to the successful implementation of computing education in primary schools.

While there is some material available in German for primary education in the field of AI and ML, it is mainly designed for older students and therefore not suitable for primary school. For example, the *AI + Ethics Curriculum for Middle School* project<fnref n="2" /> has translations into German, Portuguese and Korean on the subject of AI, but it targets older students from grade 6 to 8 (aged 12–14). Similarly, the *AI Unplugged* material [18, 24] is primarily intended for students in lower and upper secondary education (aged 12–18). Lindner et al. [18], however, point out that some activities could be used for younger students, for which they would probably need to be adapted. The material by Janssen [25] on AI, ML and facial recognition does not require any programming knowledge, which makes it initially appear more interesting for use in primary school. In addition to technical aspects, the material addresses the social, political, and ethical effects of AI systems. However, these materials are designed for older students (about 15 and 16 years), and are therefore not suitable for use in primary school. Due to the limited availability of German material on AI and ML for use in primary schools, the aim of our project is to develop and test further material. In the following, the specific conditions under which the project is carried out and first practical classroom experiences are described.

<footnote n="1">¹ https://teachablemachine.withgoogle.com/.</footnote>

<footnote n="2">² https://www.media.mit.edu/projects/ai-ethics-for-middle-school.</footnote>
# 3 Concept Development

## 3.1 Overall Conceptual Consideration

**Combining Ideas from Biology and Computer Science.** Our material was developed as part of a funded project. To combine ideas from Biology and Computer Science, we selected plant identification apps to introduce basic concepts of AI and ML. We chose this field to create unplugged teaching material because the identification of plants is part of the Primary Social and Science Education curriculum (content area “Animals, Plants, Habitats” [12]), and not the *Good-Monkey-Bad-Monkey Game* material by [18], which could lead to misconceptions about monkey behavior. The teaching material first focuses on the identification of plants without digital tools, but later also extends this process to a digital identification app for plants. During the unplugged activity, students take on the role of an AI system and complete a learning process in which they identify features of leaves and learn basic concepts of ML. The next section provides more detailed insights into the material and its usage.

**Teaching Material and Approach.** Our manual for teachers is divided into three parts: The first part discusses the curricular integration of computing education in primary social and science education. It explains how the inclusion of informatic phenomena aligns with the digitalization strategy outlined in [3] and the core curriculum for PSE in North Rhine-Westphalia [12] as well as the recommendations for primary schools provided by the German Computer Science association [15]. It also outlines the objectives and desired competencies of the module. The second part of the manual provides theoretical background information on computer science and biology that are essential for the module. This includes introducing the terms AI, ML, and decision trees in the computer science section and the structure and features of leaves in the biology section.

The third and final part of the manual presents detailed descriptions of several lessons and the associated lesson materials. The series of lessons follows a four-step approach to teach basic concepts of ML (supervised learning): 1. training data, 2. development of rules, 3. testing the model, 4. reflection.

1. The students are introduced to a set of training data, similar to an AI system. The data set consisting of 24 images of 12 different types of leaves. They are shown in pairs of photographs and schematic representations (see Fig. 1). With the self-developed memory game, the students try to find the pairs of leaves and discuss the relationships between the images and leaves. The memory game provides an interactive approach to the topic in which the students explore and practice biological technical terms for leaf components and discovering distinctive features of leaves (such as leaf shape, edge, tip). The naming of the leaf types is supported by the teacher, afterwards.

2. In the second step, rules are developed to distinguish the leaf types by creating decision trees using the distinctive features of the leaves from the memory game. This is done in a playful way by having the students try to guess a leaf
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Excerpt from the working material for the memory game.</figcaption></figure>

that the teacher has chosen, asking questions about the leaf's features (e.g., *What does the edge of the leaf look like?*?). Building on the questions asked, an incomplete decision tree is created. After that, the tree is completed by the students so that all the leaves from the memory game can be clearly identified. An incomplete decision tree is created, then completed by the students so that all the leaves from the memory game can be clearly identified. A possible resulting decision tree is shown in Fig. 2. The tree can be quite extensive, but could be reduced by decreasing the number of leaf types (memory cards). We did not use a binary decision tree here, which tends to be easier to understand, because the number of leaf features would have leaded to a more complex tree than the one shown in Fig. 2.

3. In the third step, the students test their own decision trees using unknown leaves (test data). Among them, there are also types of leaves which were not included in the memory game before. The students then discuss the limitations of the developed decision trees and add new elements to the tree, so that the unknown leaves can be identified. The opportunity to change the tree if new leaves appear gives students first insights into the limitations of AI systems (e.g., underfitting and overfitting).

4. In the final step, the students test the leaf identification app *Seek*<fnref n="3" />, make assumptions about how it works, and discuss the limitations of identification apps by means of blurred images.

The material focuses on action-oriented learning and design-based research [26] in primary schools. Action-oriented learning is designed to integrate practical tasks into the classroom [27]. Therefore, [28] introduce the concept of “learning through action”, which is opposed to that of “learning to act”. In this

<footnote n="3">³ https://www.inaturalist.org/pages/seek_app.</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 2. Possible decision tree for the twelve different leaf types.</figcaption></figure>
sense, action-oriented tasks attempt to convey subjects and material contents to students through their actions. Due to the structure of action orientation through raising an issue, planning of a solution, practical implementation and reflecting about the procedure [29], action-oriented instruction is suitable for the implementation of computing education in primary schools. Therefore, university teacher education must enable students to integrate computing education into (action-oriented) primary social and science education.

# 4 Implementation and Evaluation

The material was tested in a third-grade primary school class from March to April 2022 with 16 female students. It was used in a primary social and science education class and the teacher provided feedback on its applicability afterwards. Based on this feedback, the material was subsequently adapted.

Overall, the material could be used in class as anticipated and was found to be effective. Through the use of a memory game, students were able to identify essential features of leaves (shape, tip, and edge) and use these features to create a decision tree. However, extra guidance and moderation by the teacher was necessary during the creation of the decision tree. The extensive structure of the decision tree initially required basic knowledge on the purpose of decision trees. At the beginning, some students had difficulties in obtaining information from a decision tree. They wanted to “jump” within the tree and did not move along the edges. With additional material and explanations, this step was reasonably supported. Following these experiences, we added profiles of the corresponding leaves to the handout to enable teachers and students to failproof their work-sheets. With the profiles, the developed features of the individual leaves are recorded, so that they can be accessed during the creation of the decision trees. Because the lessons do not always have to take place in the same classroom, it is difficult to collect intermediate results on posters in the classroom. Therefore, working sheets with profiles and material for cutting out are required, which were included subsequently.

Furthermore, the material was found to provide opportunities for students to communicate and collaborate on the problem-solving task of the memory game. For example the memory game requires discussions to find the “correct” solution, and to identify features of leaves, such as, the tip of the leaf. A positive effect was found in an enhanced practice of biological technical terms.

The students successfully applied their knowledge of self-created decision trees to make assumptions about the functioning principles of the identification app *Seek*. The transfer and application of knowledge appears smoothly. They were also able to provide explanations for why the app could not identify certain leaves, such as blurred images or multiple leaves in the camera image. Through guidance from their teacher, the students were able to transfer the principle of automatic recognition and its limitations to other everyday areas, such as face detection for unlocking smartphones and its limitations when wearing a mask.

However, some issues were identified and revisions to the material were needed. It turned out that the identification app *Seek* was unable to identify
all leaves when photos of them were printed on paper. This was found to depend on the quality of the photographic material and print quality. Additionally, the variability within species caused discussions, as the photos of certain leaves (e.g. hazelnut) were ambiguous and displayed different characteristics in different photos (e.g. the tip on one photo looked rather round and more or less pointed on another).

## 4.1 Feedback by the Teacher

The supervising teacher acknowledged many positive aspects of the material. She highlighted the benefits of the material in terms of promoting communication about the subject, reinforcing biological terms and encouraging diverse, independent, and interactive work among the students. The teacher considered the material to have high potential for teaching primary social and science education, as it has many connections to biological topics covered in PSE curriculum. Furthermore, the decision trees can also be used to make connections to mathematics, as they share similarities with tree diagrams used in combinatorics. However, the teacher suggested that the material would be more appropriate for use in a fourth-grade class, due to its complexity and level of abstraction. Additionally, the teacher felt that additional concrete visualisations, such as maps, illustrations, craft forms and templates in the handbook would have been beneficial in some places to make it easier to prepare lessons. These have now been added in the revised handbook.

# 5 Conclusion and Outlook

In the future, the teaching material will be used by students during their teacher internship semester in primary social and science education studies, and will be tested to provide prospective teachers with an easy entry into AI and ML. The material will also be made available to active teachers in schools, allowing them to work independently with the material and expand their knowledge.

From a long-term perspective, incorporating the project and material into teacher education enables students to gain access to computer science during their studies and opens the possibility for them to approach digital technologies from a computer science and technical perspective. This can also help students to develop a positive computer science-related self-concept, interest in computing and, above all, skills in this area, which they can test and apply in practical contexts in school and university. This will equip prospective teachers with the skills and attitudes needed to lead digital education in schools.

## References

1. European Commission. Joint Research Centre.: DigComp 2.2, The Digital Competence Framework for Citizens: With New Examples of Knowledge, Skills and Attitudes. Publications Office, Luxembourg (2022)
2. Kammerl, R., Kramer, M.: The changing media environment and its impact on socialization processes in families. Stud. Commun. Sci. **16**(1), 21–27 (2016)

3. KMK: Bildung in der digitalen Welt - Strategie der Kultusministerkonferenz. Beschluss der Kultusministerkonferenz vom 08.12.2016 in der Fassung vom 07.12.2017, Kultusministerkonferenz (2016)

4. Brinda, T., et al.: Frankfurt-Dreieck zur Bildung in der digital vernetzten Welt. In: Pasternak, A. (ed.) Informatik für alle, pp. 25–33. Gesellschaft für Informatik, Dortmund (2019)

5. K-12 Computer Science Framework Steering Committee: K-12 Computer Science Framework. Technical Report, Association for Computing Machinery, Code.org, Computer Science Teachers Association, Cyber Innovation Center, and National Math and Science Initiative, New York (2016)

6. CECE: Informatics Education in Europe: Are We All In The Same Boat? Technical report, The Committee on European Computing Education, New York, NY, USA (2017)

7. Kuckuck, M., et al.: Informatische Bildung in Praxisphasen des Sachunterrichts in NRW. In: Humbert, L. (ed.) Informatik - Bildung von Lehrkräften in allen Phasen, pp. 241–250. No. P-313 in Lecture Notes in Informatics (LNI), Gesellschaft für Informatik e. V. (GI), Bonn (2021)

8. Benton, L., Hoyles, C., Kalas, I., Noss, R.: Bridging primary programming and mathematics: some findings of design research in England. Digit. Experiences Math. Educ. **3**(2), 115–138 (2017)

9. Hubwieser, P., Armoni, M., Giannakos, M.N., Mittermeir, R.T.: Perspectives and visions of computer science education in primary and secondary (K-12) schools. ACM Trans. Comput. Educ. **14**(2), 1–9 (2014)

10. GDSU: Sachunterricht und Digitalisierung. Positionspapier erarbeitet von der AG Medien & Digitalisierung der Gesellschaft für Didaktik des Sachunterrichts (GDSU), Gesellschaft für Didaktik des Sachunterrichts, Online-Publikation (2021)

11. European Council: Recommendation of the European Parliament and of the Council of 18 December 2006 on key competences for lifelong learning. Technical Report 2006/962/EC, Council of the European Union, Brussels (2006)

12. MSB NRW: Lehrplan für die Primarstufe in Nordrhein-Westfalen: Fach Sachunterricht. Auszug aus Heft 2012 der Schriftenreihe, Schule in NRW“, Sammelband: Lehrpläne Primarstufe, Ministerium für Schule und Bildung des Landes Nordrhein-Westfalen, Düsseldorf (2021)

13. GI: Grundsätze und Standards für die Informatik in der Schule: Bildungsstandards Informatik für die Sekundarstufe I. Beilage zu LOG IN 28 (150/151), Empfehlungen der Gesellschaft für Informatik e.V. (2008)

14. Brinda, T., Puhlmann, H., Schulte, C.: Bridging ICT and CS: educational standards for computer science in lower secondary education. In: Proceedings of the 14th Annual ACM SIGCSE Conference on Innovation and Technology in Computer Science Education - ITiCSE 2009, Paris, France, p. 288. ACM Press (2009)

15. GI: Kompetenzen für informatische Bildung im Primarbereich. Beilage zu LOG IN 39 (191/192), Empfehlungen der Gesellschaft für Informatik e.V. (2019)

16. Dagienė, V., Jevsikova, T., Stupurienė, G., Juškevičienė, A.: Teaching computational thinking in primary schools: worldwide trends and teachers’ attitudes. Comput. Sci. Inf. Syst. **19**(1), 1–24 (2022)

17. McGarr, O., Mcdonagh, A.: Digital Competence in Teacher Education. Output 1 of the Erasmus+ Funded Developing Student Teachers’ Digital Competence (DICTE) Project., DICTE (2019)
18. Lindner, A., Seegerer, S., Romeike, R.: Unplugged activities in the context of AI. In: Pozdniakov, S.N., Dagienė, V. (eds.) ISSEP 2019. LNCS, vol. 11913, pp. 123–135. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33759-9_10

19. Shamir, G., Levin, I.: Teaching machine learning in elementary school. Int. J. Child-Comput. Interact. **31**, 100415 (2022)

20. Kim, S., Lee, M., Kim, H., Kim, S.: Review on artificial intelligence education for k-12 students and teachers. J. Korean Assoc. Comput. Educ. **23**(4), 1–11 (2020)

21. Williams, R., Park, H.W., Oh, L., Breazeal, C.: PopBots: designing an artificial intelligence curriculum for early childhood education. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 9729–9736 (2019)

22. Voulgari, I., Zammit, M., Stouraitis, E., Liapis, A., Yannakakis, G.: Learn to machine learn: designing a game based approach for teaching machine learning to primary and secondary education students. In: Interaction Design and Children, Athens Greece, pp. 593–598. ACM (2021)

23. Toivonen, T., Jormanainen, I., Kahila, J., Tedre, M., Valtonen, T., Vartiainen, H.: Co-designing machine learning apps in K–12 with primary school children. In: 2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT), Tartu, Estonia, pp. 308–310. IEEE (2020)

24. Ossovski, E., Brinkmeier, M.: Machine learning unplugged - development and evaluation of a workshop about machine learning. In: Pozdniakov, S.N., Dagienė, V. (eds.) ISSEP 2019. LNCS, vol. 11913, pp. 136–146. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33759-9_11

25. Janssen, D.: Machine Learning in der Schule: Eine praxisorientierte Einführung in künstliche neuronale Netze, Gesichtserkennung und Co., 2nd edn. Science on Stage Deutschland e.V., Berlin (2020)

26. Van den Akker, J., Gravemeijer, K., McKenney, S., Nieveen, N. (eds.): Educational Design Research, Zeroth edn. Routledge (2006)

27. Jank, W., Meyer, H.: Didaktische Modelle. Cornelsen, Berlin, 14. auflage edn. (2021)

28. Möller, K.: Handlungsorientierung im Sachunterricht. In: Kahlert, J., Fölling-Albers, M., Götz, M., Hartinger, A., Miller, S., Wittkowske, S. (eds.) Handbuch Didaktik des Sachunterrichts, 2nd edn., pp. 403–407. Schulpädagogik, Klinkhardt, Bad Heilbrunn (2015)

29. McConnell, J.J.: Active learning and its use in computer science. In: Proceedings of the 1st Conference on Integrating Technology into Computer Science Education, Barcelona, Spain, pp. 52–54. ACM Press (1996)