9

Reflections and Concluding Remarks

In keeping with the statement of task (see Chapter 1), the workshop brought together international experts in secondary mathematics to discuss how their countries are modernizing their curricula. They shared how computational thinking, data science, and artificial intelligence (AI) are being addressed across four themes: (1) curriculum design and policy implementation, (2) assessment techniques and student learning, (3) technology tools and frameworks, and (4) instructional approaches and professional practice. The workshop was designed to provide opportunities at the end of each afternoon for participants to reflect on points of particular resonance.

DAY ONE: INSIGHTS, TENSIONS, AND OPPORTUNITIES

Planning committee chair Padhu Seshaiyer looked back at the agenda for the first day, with Conrad Wolfram’s keynote address (Chapter 2), a session on curriculum and policy implementation (Chapter 3), a session on assessment and student learning (Chapter 4), and the mixed mini-session (Chapter 5). Seshaiyer first asked in-person participants to reflect on their reactions individually and capture them in Post-it notes. Then he asked them to discuss them informally with other neighboring attendees and solicited a volunteer per group to provide a brief summary of the discussions to the entire workshop.

He summarized some of the themes he heard that emerged from the exercise, noting that each comes from an individual respondent and does not necessarily represent the views of all workshop participants.

The “Zero-Sum” Curriculum Dilemma

The most consistent tension expressed by participants is the “flooded” nature of modern curricula, he said. If AI and data science are added, several asked, what is removed? Seshaiyer highlighted some of the comments in this regard:

• The Content Trade-off: Several participants suggested a “repurposing” or “deletion” of old content to make room for the new.
• Integration versus Isolation: There is an ongoing debate on whether data science and AI should be standalone subjects or integrated into mathematics.
• Radical Shifts: Inspired by the keynote and examples from South Korea, several participants noted a possible shift from “mastering mechanics” to “high-level conceptualization” and “exploratory microworlds.”

Professional Agency and Policy Infrastructure

Seshaiyer said he agreed with the comments from some participants that policy is currently lagging behind innovation. For any curriculum change to succeed, the “human infrastructure” (teachers) must be supported, including the following:

• Paid Professional Development: Many noted the “Korea/Ireland/New Zealand model,” where teachers are given paid time during the workday for training, rather than it being an “add-on” responsibility.
• The Professional Paradox: In the United States and some other countries, local control makes national implementation difficult, whereas countries like Singapore and Scotland benefit from more centralized or industry-supported training.
• Teacher Load: Participants viewed AI through two lenses: a new subject to teach (burden) and a tool to reduce administrative load (boon).

From Standardized Testing to “Authentic Assessment”

Many participants commented that traditional standardized testing is the “wrong starting point” for AI education, Seshaiyer noted, and added the following:

• Process over Product: A participant observed the shift toward assessing cognitive skills, mathematical modeling, and investigations in Australia, rather than rote knowledge.

• New Formats: Reference was made to several ideas presented at the workshop, including computational notebooks in Austria and mixed-format assessments in several countries that value how a student arrives at a solution, not just the answer.
• The Funding Gap: Authentic, scalable assessment was noted as being “complex and expensive,” requiring significant policy-level investment.

Equity and the New Digital Divide

Some of the discussions highlighted a shift from “access to hardware” to “access to mastery,” Seshaiyer pointed out:

• The 36 Percent Gap: One group reflected on the statistic presented earlier that more than one-third of the global population cannot use generative AI due to lack of support or Internet access, threatening to widen the global “digital gap.”
• Diverse Learners: Another group raised concerns for students in the arts or those with “no talent” in science, technology, engineering, and mathematics (STEM), and queried how an AI-centric curriculum remains inclusive for them.
• Ethics and Rights: The focus on the rights of the child in Chile, Singapore, Scotland, and Finland and the ethical implications of AI were pointed to as ways to ensure students are considered “citizens” and not just “users.”

Redefining “Mastery” in the Machine Age

The final set of comments that Seshaiyer shared noted that the presence of AI forces a reevaluation of what it means to be a “student.” He highlighted the following:

• Don’t Compete with the Machine: Echoing Wolfram’s philosophy, the focus is shifting toward “skills for life”: adaptability, critical thinking, and knowing “the right tool for the job.”
• Math Anxiety: There is an unanswered opportunity for AI to act as a supportive tool to lower the barrier to complex mathematical thinking.
• Human-Centric Skills: An emphasis on unplugged activities in Finland and Scotland is a way to ensure that, even in a high-tech world, fundamental human logic and creativity remain the priority.

DAY TWO: MOVING FORWARD

The exercise at the conclusion of the second day brought together the sessions across both days of the workshop, including, in addition to the above, the second keynote, by Junaid Mubeen (Chapter 6), the session on technology tools and frameworks (Chapter 7), and the final session on instruction (Chapter 8).

To set up this final exercise, Seshaiyer drew on an approach from the operations research community in which matrices help elucidate costs and benefits, risks and rewards, and other countervailing constraints. The matrix he proposed presented low to high effort on the x axis and low to high impact on the y axis. A “high-impact, low-effort” project, for example, would probably be pursued as a “quick win,” while a “low-impact, high-effort” option would be avoided. Those that may or may not be pursued are those that are “low-effort, low-impact” and “high-effort, high-impact” projects.

Similar to the process followed at the end of Day 1, participants worked individually and then in small groups before presenting their ideas across the matrix quadrants. Seshaiyer summarized some of the themes he heard that emerged from the exercise, noting that each suggestion comes from an individual respondent and does not necessarily represent the views of all workshop participants. Overall, he said, he noticed a clear shift from resource sharing and messaging (quick wins) toward systemic reform and teacher empowerment (long-term goals).

High Impact and Low Effort (Quick Wins)

These are “low-hanging fruit” actions, mostly focused on advocacy, curation, and open collaboration, Seshaiyer commented, to include

• Global Advocacy and Policy Sign-on: Establishing a unified voice through global pledges (equal opportunity letters) and securing high-level backing from organizations like the World Bank or Organisation for Economic Co-operation and Development. (Group 1)
• Open-Source Resource Exchange: Rapidly sharing “bite-sized,” Creative Commons–licensed, and “plug-and-play” materials (unplugged activities, no-code approaches, and real-world datasets like the Sustainable Development Goals) that teachers can use immediately. (Groups 3, 4, 5)
• Teacher-Centric Messaging: Focusing on AI as a tool to reduce teacher workload and providing clear, nonintimidating rubrics and guidelines for classroom decision making. (Groups 2, 5)

• Collaborative Research and Scales: Developing shared metrics (e.g., data self-efficacy scales) and innovation engines to avoid duplicating effort across regions. (Groups 2, 4)
• Contextualized Learning Data: Developing case studies with real-world data (such as the United Nations sustainable development goals); instruction in sustainability and integrating computational thinking, data science, and AI through “contexts.” (Groups 2, 4)

High Impact and High Effort (Long-Term Strategic)

These represent fundamental shifts in the educational ecosystem that require significant time, funding, and cultural change, to include the following:

• Comprehensive Teacher Professional Development (PD): Moving beyond one-off workshops to “targeted, ongoing” PD that builds teacher confidence, integrates psychology with content knowledge, and makes the “invisible visible.” (Groups 2, 4, 5)
• Curriculum and Discipline Reimagination: A total rebranding of mathematics to integrate computational thinking, data science, and AI natively. This includes breaking down the silos between mathematics and computer science and moving toward “context-based” learning. (Groups 2, 4)
• Assessment Reform: Designing and implementing global assessment banks and high-stakes testing that measure computational literacy and “student thinking” rather than just rote knowledge. (Groups 2, 5)
• Systemic Policy and Infrastructure (Tools and Inclusion): Formulating top-down standards across K-16, creating microcredentials for competency, and addressing global inequities like Internet access. (Groups 3, 4, 5)

Low Impact and Low Effort (Short-Term Maintenance)

These items were noted as having less transformative power but being relatively easy to maintain or implement, Seshaiyer explained:

• Localized Tool Adoption: Funding or using existing specific tools (like those from Finland) without necessarily scaling them globally. (Groups 3, 4)
• Isolated Skill Building: Focusing on general technology skills in children or designing tools for integration management which, while helpful, may not move the needle on deep mathematical or

• computational thinking understanding compared to other initiatives. (Groups 3, 4)

Low Impact and High Effort (The “Avoid” Zone)

While the groups did not explicitly list items that required high effort for low impact, Seshaiyer said he picked up on several warning areas based on their feedback:

• Starting from Scratch: Multiple groups noted that reinventing the wheel or creating whole new curricula from zero is a high-effort trap; the quick win is to adapt and regionalize existing open-source materials.
• Humanizing AI: Group 5 explicitly stated to “avoid humanizing AI” as a strategic stance, suggesting that focusing energy on the “personality” of AI rather than its pedagogical utility is a poor use of resources.

Some groups also categorized several ideas as “middle ground,” focusing on implementation nuances related to the adoption of technology for specific classrooms (Group 2) and outcomes and processes for computational literacy (Group 5).

CONCLUDING REMARKS

Seshaiyer again thanked presenters and attendees for their participation in the workshop and pointed to development of a repository of workshop materials and an upcoming webinar series. He called attention to the designation of 2026 as the Year of Mathematics, facilitated in the United States by the Conference Board of the Mathematical Sciences.¹

Zarek Drozda underscored the value of the convening and of further collaboration and networking. He commented on a tendency to feel discouraged after some workshops given the gaps and barriers identified in the course of the discussions. However, he said, he heard many ideas that are already working in different countries and contexts, keeping in mind Wolfram’s call for larger changes as well. The idea that people from 17 to 20 countries came together is also powerful, he added, and posed whether some kind of advocacy or research undertaken together could follow.

Farshid Safi said he was energized by the sessions and eager to continue the conversations. He urged collaborative, systematic ways to share knowledge, such as the upcoming conference of the Association of Mathematics

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¹ For more information, see https://theyearofmath.org

Teacher Educators. As another example, David Weintrop highlighted an effort by Transforming Post-Secondary Education (TPSE) to convene research mathematicians to begin to consider elements that may be taken out of the mathematics curriculum, with all the new areas to be included. Another TPSE effort is looking at mathematics at the undergraduate level.

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