Professional Development for Teachers in AI Literacy Education:

Teaching Machine Learning to Senior Primary and Junior Secondary

Students

Yin Yang

and Siu Cheung Kong

1,2* b

Department of Mathematics and Information Technology, The Education University of Hong Kong, HKSAR, China

Artificial Intelligence and Digital Competency Education Centre, The Education University of Hong Kong, HKSAR, China

Keywords: AI Literacy Education, K-12 Educational Settings, Machine Learning, Teacher Professional Development.

Abstract: This paper presents a study focused on equipping in-service teachers’ skills of delivering courses on teaching

machine learning concepts to senior primary and junior secondary school students. The pedagogical design

of this study was based on the neuroscience-informed Attention-Engagement-Error-feedback-Reflection

(AEER) framework. This study involved 36 in-service teachers from Hong Kong primary and secondary

schools. We developed a model supported by Workshops, Discussions, and Resources (WDR) within the

framework of Technological Pedagogical Content Knowledge (TPACK) to design the teacher professional

development program. Data collection included pre- and post-tests on AI concepts, as well as pre- and post-

questionnaires on using robots to teach machine learning on their TPACK, teachers’ written feedback on the

professional development. The findings suggest that the integration of using robots to teach machine learning

and guided by a transdisciplinary pedagogical design AEER motivated teachers to teach AI literacy in senior

primary and junior secondary schools. Furthermore, the workshops notably improved teachers’ perceptions

of their TPACK abilities. The implications for the professional development on equipping teachers for AI

literacy education are summarised.

1 INTRODUCTION

In the rapidly evolving digital world, Artificial

Intelligence (AI) has become a pivotal force in

education. It is crucial that the next generation is not

only adept at using these technologies, but also

understands and shapes them. Thus, AI literacy

emerges as a fundamental skill in the 21st-century

educational settings (Casal-Otero et al., 2023).

While the integration of AI into educational

settings presents vast opportunities, it also poses

significant challenges, particularly in terms of

curriculum development and teacher readiness for

teaching machine learning in K-12 educational

settings (Rauber et al., 2022; Sanusi et al., 2023).

Primary and secondary education systems often

struggle to keep pace with technological

advancements due to outdated teaching methods and

a lack of professional development.

https://orcid.org/0000-0002-9966-248X

https://orcid.org/0000-0002-8691-3016

It is important for students to understand the

mechanisms behind the technologies that permeate

their daily lives. Educational initiatives that connect

K-12 computing education with students’ everyday

technological interactions aim to close this gap

(Gresse von Wangenheim et al., 2021; Touretzky et

al., 2019; Van Mechelen et al., 2023).

Without a basic understanding of machine

learning principles, many applications and services

that children regularly engage with might seem

inexplicable. For example, smartphones unlock with

a glance at their owner’s face; and home assistants

respond to voice commands. It is essential to

educate students that these functionalities, while

sophisticated, do not equate to human-like

intelligence (Karalekas et al., 2023). However, very

limited studies have been conducted to guide teachers

to teach AI for young learners (Su& Zhong, 2022).

This paper aims to address these challenges by

examining a study focused on professional

Yang, Y. and Kong, S. C.

Professional Development for Teachers in AI Literacy Education: Teaching Machine Learning to Senior Primary and Junior Secondary Students.

DOI: 10.5220/0013200300003932

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Conference on Computer Supported Education (CSEDU 2025) - Volume 2, pages 35-42

ISBN: 978-989-758-746-7; ISSN: 2184-5026

development for in-service teachers. This study

contributes to the emerging field of AI education by

demonstrating how innovative tools (e.g. AlphAI

learning robots) and pedagogies can be effectively

utilised in teacher professional development. The

goal is to provide insights into scalable and

sustainable approaches for augmenting AI literacy in

foundational education settings. We designed the

teacher professional development using the WDR

(workshops, discussions, and resources)-supported

TPACK model. A comprehensive 6-hour workshop

was designed to deepen teachers’ understanding of

machine learning concepts and facilitate discussions

on AI in transdisciplinary education, along with

providing substantial teaching materials

The study is guided by two research questions:

Research question 1: How does professional

development using the WDR-supported TPACK

model affect teachers’ understating of machine

learning concepts?

Research question 2: How does professional

development using the WDR-supported TPACK

model affect teachers’ ability of integrating learning

robots with teaching machine learning?

2 LITERATURE REVIEW

2.1 Machine Learning in K-12

Educational Settings

The integration of machine learning in K-12

education is increasingly important as it prepares

students for a technology-driven future. With the

rapid advancement of AI technologies, understanding

machine learning concepts is essential not only for

future careers but also for fostering critical thinking

and problem-solving skills. Studies have explored

innovative approaches, such as teachable machines,

which empower students to create and train their own

machine learning models (Gresse von Wangenheim

et al., 2021; Tedre et al., 2021).

However, despite these advancements, significant

gaps remain in the widespread integration of machine

learning education in K-12 settings. One major gap is

the lack of comprehensive pedagogical framework

accommodate the diverse educational needs across

different educational standards (Yue et al., 2022). Our

previous studies have proposed an innovative

pedagogical framework, the Attention-Engagement-

Error-feedback-Reflection (AEER) framework,

specifically designed for teaching machine learning

in primary schools (Kong & Yang, 2023; 2024a).

These studies indicated that the AEER framework

could significantly increase student motivation,

engagement, and understanding of ML concepts.

Nevertheless, a notable challenge is that in-

service teachers often lack the necessary training and

confidence to effectively teach machine learning

concepts (Antonenko & Abramowitz, 2023;

Sulaiman et al, 2022).

2.2 Teacher Professional Development

in Teaching Machine Learning

Among the limited studies, some have investigated

various pedagogical tools and frameworks to equip

teachers with the necessary skills to teach machine

learning effectively (Lin & Van Brummelen, 2021).

Other research has highlighted effective instructional

methods, including project-based learning (Ossovski

& Brinkmeier, 2019) and problem-based learning

(Kim et al., 2021). The importance of teacher

professional development cannot be overstated; well-

trained educators are crucial for implementing

effective ML curricula and fostering a supportive

learning environment.

The framework of TPACK has been a

comprehensive framework that identifies essential

elements of teacher knowledge needed for successful

technology integration in education (Koehler &

Mishra, 2009; Mishra & Koehler, 2006). In this study,

we focused on the four components: Content

Knowledge (CK), Pedagogical Content Knowledge

(PCK), Technological Content Knowledge (TCK),

and the overarching blend of TPACK because the CK

of machine learning is the focus for teaching in the

professional development (Kong et al, 2020).

CK refers to the understanding of the subject

matter that educators are teaching. For instance, a

teacher must grasp key concepts in machine learning,

such as K-nearest neighbours (KNN), artificial neural

networks (ANN) (e.g., input layer, hidden layer,

output layer, backpropagation), supervised learning,

and reinforcement learning. Mastery of CK enables

teachers to present information accurately and

comprehensively, which is vital for student

understanding. For example, knowing how to explain

complex topics such as backward propagation and

overfitting in supervised learning is essential for

fostering student comprehension in these areas.

TCK involves understanding how technology can

be effectively applied to teach specific subject matter.

In the context of teaching machine learning, TCK

includes the ability to manipulate the parameters of

software platforms, such as using AlphAI robots in

this study (https://learningrobots.ai/?lang=en). For

instance, a teacher must know how to adjust settings

CSEDU 2025 - 17th International Conference on Computer Supported Education

to illustrate the workings of an ANN or to set up an

environment for observing reinforcement learning.

PCK combines teaching strategies with subject

matter knowledge. It involves knowing how to teach

specific content effectively. For example, using the

AEER pedagogy (refer to section 2.3, Kong & Yang,

2023; 2024a), a teacher can create lesson plans that

encourage active participation, provide constructive

feedback, and foster reflection among students.

TPACK is the synthesis of CK, PCK, and TCK. It

represents a teacher’s ability to integrate technology

(AlphAI learning robots) into pedagogy while

effectively conveying content knowledge. When

teaching machine learning concepts, a teacher should

not only have a strong grasp of the content and

effective use of AEER pedagogical framework but

also be proficient in using the AlphAI robots.

2.3 A Transdisciplinary Pedagogical

Framework:

Attention-Engagement-Error-

Feedback-Reflection (AEER)

Teaching machine learning encourages educators to

reflect on their instructional methods and how they

facilitate students’ ability to learn independently.

Unlike machines, which rely on algorithms, humans

possess a unique capacity for continuous learning

(Chen & Liu, 2022).

In our ongoing research, we have refined the

AEER framework originally proposed in earlier work

(Kong & Yang, 2022; 2023). This framework has

now been actively taught to in-service teachers in

both primary and secondary educational settings. The

AEER model — comprising Attention, Engagement,

Error-feedback, and Reflection — aims to improve

student AI learning experiences by integrating

practical, hands-on activities using AlphAI learning

robots.

Attention focuses on capturing students’ interest

through activities and relevant content. Teachers were

taught to attract students’ attention to identity the key

information in learning. Engagement encourages

active participation, allowing students to immerse

themselves in the learning process. Error-feedback is

a critical component where students learn to see

mistakes as valuable learning opportunities. Students

observed errors made by robots during training, such

as hitting walls and getting stuck, and adjusted their

strategies accordingly. The teachers guided students

in understanding and rectifying errors made by the

robots and helping them understand the importance of

seeking for feedback in learning. This process not

only helps students understand the iterative nature of

machine learning but also the critical importance of

feedback in learning.

Reflection is the final element where teachers

guide students to reflect on what they have learned.

This involves discussing the learning process,

reviewing key concepts, and sharing experiences with

peers to consolidate knowledge and insights.

The AEER pedagogical framework was used to

guide teachers to teach machine learning concepts. At

the same time, teaching machine learning provides

students opportunities to reflect on learning. This

framework transcends traditional educational

boundaries. Teachers guide and facilitate, but they

also learn from the students’ experiences. In addition,

the AEER framework encourages learning through

failure, reflecting real-world scenarios where trial and

error lead to innovation and discovery.

3 RESEARCH DESIGN

3.1 Research Procedure

The teacher professional development was guided by

the WDR-supported TPACK model (Figure 1). The

program includes six one-hour face-to-face training.

The primary objective was to help teachers

understand the concept of machine learning using

AlphAI robots, the AEER pedagogical framework to

deliver the workshops to primary school students.

The professional development was supported by (1)

face-to-face workshops: introducing AlphAI learning

robots, KNN, ANN (e.g., input layer, hidden layer,

and output layer), reinforcement learning,

backpropagation, overfitting concepts, etc; (2)

discussions on human-AI relations guided by the

AEER pedagogical framework; and (3) available

teaching resources and support provided by the

research team.

Figure 2: The WDR-supported TPACK model.

Professional Development for Teachers in AI Literacy Education: Teaching Machine Learning to Senior Primary and Junior Secondary

Students

3.1.1 Face-to-Face Workshops

The face-to-face workshops serve as the core

component of the professional development program,

where teachers have hands-on activities with AlphAI

learning robots (Figure 3). Teachers first learned

about the functionalities of AlphAI robots, which are

used as a tool to demonstrate and explore machine

learning concepts (Figure 4). Detailed sessions on

KNN (K-nearest neighbours), ANN (Artificial Neural

Networks), and other algorithms such as

reinforcement learning and backpropagation were

introduced. Each session includes visual

demonstrations and interactive activities to help

teachers understand how these algorithms process

data and learn. Teachers worked as a group to analyse

how robots react to errors, learn about overfitting, and

discuss strategies for optimising machine learning

models.

Figure 3: Teachers training the AlphAI learning robots in

the face-to-face workshops.

Figure 4: The interface of the AlphAI learning robot

software visualising the ANN.

3.1.2 Discussions on Human-AI Relations

Guided by the AEER pedagogical framework, the

discussions on human-AI relations aims to deepen

teachers’ understanding of AI’s capabilities and

limitations and to explore the ethical, social, and

educational implications of integrating AI into the

classroom. For example, backpropagation, a

fundamental algorithm used for training neural

networks, where the network learns from errors by

adjusting its weights to minimise the difference

between the actual output and the desired output. This

method exemplifies the iterative and error-based

learning process of AI, which contrasts significantly

with human cognitive processes (Lillicrap et al.,

2020). We believe these discussions are vital in the

professional development programmes as they

provide teachers with direct opportunities to shape

how young minds understand and interact with AI

technologies.

Figure 5: The illustration of backpropagation using AlphAI

learning robots.

3.1.3 Available Teaching Resources

To facilitate the effective implementation of AI

concepts in classrooms, teachers were equipped with

a variety of teaching resources and support

mechanisms provided by the research team. Figure 6

shows one page of the worksheets. In addition, the

WhatsApp group was set up to allow the research

team to provide ongoing support and updates for

teachers to handle various teaching scenarios.

Figure 6: The interface of the AlphAI learning robot

software visualising the ANN.

3.2 Participants

The purposeful sampling approach was adopted (Rai

& Thapa, 2015). The teachers from the selected

CSEDU 2025 - 17th International Conference on Computer Supported Education

school had previously worked closely with the

researchers on other research projects. They had at

least three years of technology-enhanced teaching

experience. Thirty-eight teachers (females = 34.2%,

males = 65.8%) from seven primary schools and one

secondary school participated. Signed consent forms

were obtained from their schools and participants

before the study. A total of 36 responses were

returned. This study, guided by the TPACK model

supported by the WDR and utilising the AEER

pedagogical framework alongside AlphAI robots,

specifically targeted teachers experienced in

technology-enhanced teaching to explore the initial

impact of integrating machine learning concepts into

K-12 education.

3.3 Data Collection and Analysis

This study included both qualitative and quantitative

data: (1) pre- and post-tests on machine learning

concepts, (2) pre- and post-surveys on TPACK using

a five-Likert scale (ranging from strongly disagree 1

to strongly agree 5), and (3) teachers’ written

reflections.

The machine learning concept test was designed

to assess conceptual understanding in machine

learning and deep learning (refer to Appendix I). The

test was designed based on Bloom’s taxonomy and

comprised 13 items, with a Cronbach’s alpha above

0.88. To be specific, for the knowledge and recall,

two items tested students’ ability to recognise and

recall facts related to machine learning procedures.

One item required students to identify the correct

terminology for nodes in an ANN. One item involved

recalling the definition of reinforcement learning.

One item tested recall of the function and

implementation of the backpropagation method in

neural networks. For the comprehension, one item

examined students’ understanding of the machine

learning process, particularly the application of

supervised, unsupervised, and reinforcement

learning. One item focused on comprehension of how

the KNN based on the proximity of data points. Two

items required understanding of the statement

regarding supervised learning and unsupervised

learning. Regarding the application, one item tested

the ability to apply knowledge of supervised learning

by selecting the most suitable method for robot

navigation around obstacles. One item assessed the

application of backpropagation to minimise errors in

model predictions through iterative weight

adjustments. One item involved applying knowledge

to identify the best approach for creating a dataset to

train an AI model to recognise different cats. One

item focused on applying a simple computational

technique, using a pre-trained CNN for recognizing

flower types from images. For the analysis, one item

involved analysing an image representation to

determine if a model is exhibiting overfitting by

evaluating differences in performance on training

versus test data.

The questionnaire on TPACK includes 15 items

across four aspects: TCK, CK, PCK, and TPACK

(refer to samples in Appendix II). It was adapted from

a validated version (Kong et al., 2024). Cronbach’s

alpha of four aspects is all above 0.89, indicating a

good consistency of the instrument.

For the data analysis, descriptive data analysis and

paired sample t-test and a Wilcoxon signed-rank test

were used. For the RQ2, teachers’ written feedback

was also analysed to triangulate the results.

4 RESULTS

4.1 Machine Learning Concepts

A paired-samples t-test was used to determine whether

there was a statistically significant mean difference in

the conceptual understanding of the machine learning.

The assumption of normality was not violated, as

assessed by Shapiro-Wilk’s test. The total score of the

machine learning concepts significantly increased,

diff

= 3.91, SD = 2.62, 95% CI = [3.03, 4.80] t(35) =

8.96, p < .001. Table 1 shows the descriptive data of

each machine learning concept item.

Table 1: The descriptive data of machine learning concepts.

Pre Post

Mean SD Mean SD

Item1 .61 .49 .97 .17

Item2 .61 .49 .97 .17

Item3 .75 .44 .92 .28

Item4 .58 .50 .97 .17

Item5 .47 .51 .97 .17

Item6 .64 .49 .92 .28

Item7 .75 .44 .92 .28

Item8 .69 .47 .94 .23

Item9 .36 .49 .61 .49

Item10 .67 .48 .86 .35

Item11 .81 .40 .92 .28

Item12 .28 .45 .42 .50

Item13 .19 .40 .94 .23

4.2 TPACK Survey

For the second research question, a Wilcoxon signed-

rank test was employed because the TPACK data did

Professional Development for Teachers in AI Literacy Education: Teaching Machine Learning to Senior Primary and Junior Secondary

Students

not meet the normality assumption required for a

paired-samples t-test. Table 2 shows the descriptive

data of TPACK.

Table 2: The descriptive data of TPACK.

Mean SD Skewness Kurtosis

PreTC

3.046 1.024 -.170 -.474

PreC

2.956 .888 -.296 .238

PrePC

2.840 .943 -.423 -.539

PreTPAC

2.833 .997 -.197 -.306

PostTC

4.250 .745 -2.222 9.525

PostC

4.222 .728 -2.282 10.242

PostPC

4.243 .773 -2.051 7.852

PostTPAC

4.213 .781 -1.989 7.212

A Wilcoxon signed-rank test showed that there is

a statistically significant change in teachers’

perceived ability of TCK (Z = 4.634, p < 0.001), CK

(Z = 4.744, p < 0.001), PCK (Z = 4.782, p < 0.001),

and TPACK (Z = 4.809, p < 0.001). Figure 7 shows

the bar chart of the comparison between pre-and post-

survey.

Figure 7: Teachers’ pre- and post-perceived skills in TCK,

CK, PCK, and TPACK.

4.3 Teachers’ Reflections

Thematic analysis on participants’ written feedback

revealed several major themes on teachers’

perceptions towards the training, including (1)

enhanced machine learning concepts, (2) effective

use of AlphAI robots under the AEER pedagogical

framework to facilitate the teaching of machine

learning concepts, and (3) strengthened confidence in

teaching AI. One of the authors and a research

assistant analysed the qualitative data. Initially, 30%

of the data was coded collaboratively by both

researchers to establish a consistent coding

framework. After developing a shared understanding,

each researcher independently coded the rest of the

data. To assess the consistency of the thematic

analysis between the two coders, interrater reliability

was calculated after the independent coding phase.

The reliability score was above 0.85, indicating a high

level of agreement between the researchers. Any

disagreements in coding were discussed and resolved

through consensus. This study selected some

examples from teachers written reflection.

Teachers noted that the detailed instructions

provided during the course were particularly helpful.

Firstly, the workshop clarified machine learning

concepts, as Teacher B observed: “The hands-on

experience with the robots allowed me to really grasp

how to explain complex concepts like neural networks

in simple terms that my students can understand.”

Secondly, AlphAI learning robots were perceived

as useful tools to help primary school students

visualise the algorisms and neuro networks. Teacher

F reflected, “Using the AlphAI robots made it

tangible for the students and for myself. It visualised

a lot of preconceived notions about the complexity of

machine learning”. Another teacher highlighted the

benefits of the AEER framework: “What really stood

out to me was the AEER framework. It is like we had

a roadmap for engaging our students effectively,

providing feedback, and then reflecting on it to make

learning even better.”

Thirdly, teachers reported an increase in

confidence regarding their ability to teach and

integrate AI concepts into their curricula. As Teacher

C reported: “Honestly, I was a bit skeptical at first

about how much I could really learn from just 6-hour

workshop, but I am blown away! The way we were

able to actually interact with the robots and see

firsthand how the algorithms work—it is like a light

bulb went off! I cannot wait to show my kids these

concepts; they're going to love it.”

Overall, the majority of teachers expressed

satisfaction with the professional development

workshops for teaching machine learning concepts.

One teacher suggested, “Can we do more of these

workshops? The hands-on element, the clear

explanations, the supportive atmosphere—it's exactly

what we need to keep ourselves and our teaching

methods up-to-date.” Teachers’ written feedback

showed the effectiveness of the training in enhancing

teachers’ capabilities to engage and educate their

students on complex technological subjects.

5 CONCLUSIONS

This study provided significant insights into the

efficacy of professional development workshops that

utilised the AEER pedagogical framework and

AlphAI robots in enhancing primary school teachers’

understanding and teaching of machine learning

concepts.

3.046

2.956

2.84

2.833

4.25

4.222

4.243

4.213

TCK

TPACK

CSEDU 2025 - 17th International Conference on Computer Supported Education

The research and practical implications of this

study are discussed. First, the findings of this study

contribute to the literature on educational technology

by demonstrating how tangible tools such as AlphAI

robots can demystify complex technological concepts

like machine learning and neural networks. In

addition, the effectiveness of the WDR-supported

TPACK model highlights the need for structured yet

flexible teacher professional development needs.

From a pedagogical perspective, the study

reinforces the value of professional development in

equipping teachers with not only the technical

knowledge but also the pedagogical strategies

necessary for integrating AI into K-12 educational

settings. The increased confidence among teachers

suggests that well-designed workshops can empower

educators. Schools and educational policy makers

should consider incorporating WDR-supported

TPACK model in teacher training programs to ensure

educators are well-prepared to meet the challenges of

modern educational demands.

This pilot study on the use of AEER pedagogical

framework and AlphAI robots in teaching machine

learning concepts in K-12 education highlights the

potential benefits of integrating pedagogical

framework with robots (Camilleri, 2017).

The findings of the study also showed well-

structured teacher professional development

programs that incorporate both theoretical knowledge

and practical applications can enhance teachers’

confidence in delivering AI courses in senior primary

and junior secondary schools. The confidence, an

aspect of professional development is often

overlooked but is essential for the practical

application of new teaching strategies. Confidence in

their own understanding allows teachers to creatively

adapt AI teaching methods across various subjects,

promoting a more integrated and innovative

educational approach.

In conclusion, the workshop not only enhanced

the teachers’ understanding and ability to teach AI

concepts but also significantly improved their

pedagogical strategies, confidence, and enthusiasm

for integrating technology into education.

This study highlights the critical role of teacher

professional development in adapting education to

the age of digital technology (Hu et al., 2023; Kong

& Yang, 2024b). As AI continues to shape various

sectors, the education sector must not fall behind.

Professional development programs that incorporate

current technologies and effective pedagogical

strategies are essential for preparing teachers to

facilitate an education that equips students with the

necessary skills and knowledge to thrive.

The study, however, is limited by its short

duration, and limited follow-up. Future research will

involve longitudinal studies to track the sustained

impact of these workshops on teachers’ instructional

practices over time. In addion, conducting studies

across various educational contexts, including

different school districts or countries, could provide

insights into the scalability and adaptability of the

AEER framework. This would also help identify

contextual factors that influence the effectiveness of

such technologies and frameworks in teacher

education.

ACKNOWLEDGEMENTS

The authors would like to acknowledge the funding

from the Central Reserve Allocation Committee, The

Education University of Hong Kong of the Hong Kong

Special Administrative Region, China (Uncovering the

"Black Box" of Machine Learning: Promoting

Artificial Intelligence Literacy with AlphaAI robots in

Senior Primary/Junior Secondary Schools across Hong

Kong and France). (Project No. EdUHK 04A55).

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APPENDIX I

Machine Learning Concepts

https://docs.google.com/document/d/1pysZ1PD48a

My6UOCmKknnt2ta07VRdb9QyDdHFkbqBM/edit

?usp=sharing

APPENDIX II

TPACK Survey

https://docs.google.com/document/d/1U2TlnlvHHke

GiYfTXZBpjsSfirk6z9ufKjJ1TVUzYiM/edit?usp=s

haring

CSEDU 2025 - 17th International Conference on Computer Supported Education