While conducting a workshop for business calculus students one day, I was reminded of a familiar challenge. Many students approach calculus with hesitation, especially those in business programs who may not see themselves as “math people.” Even when they are following the steps, it is not always clear that real understanding is happening.

During the workshop, we were discussing inflation as an example of exponential growth. I wrote the model on the board and explained how prices increase over time. Students were taking notes, but their expressions suggested they were still trying to connect the formula to its meaning.

So I opened Maple Learn.

I entered the equation, and the graph appeared right beside it. Almost immediately, the mood in the room shifted. One student leaned forward and said, “Oh… that’s what inflation looks like over time.”

That simple moment captured why visualization matters so much in calculus.

One of the strengths of Maple Learn is how naturally it combines symbolic work and graphical representation in a single space. Students can write equations, perform calculations, and see the corresponding graphs without switching tools. This makes abstract ideas feel more concrete and easier to interpret.

Maple Learn also works well as a note-taking tool. During the workshop, students kept their formulas, graphs, and written explanations together in one organized document. Instead of passively copying, they were actively building understanding as they worked through the example.

What stood out most was how easily students began sharing their work. They compared graphs, discussed small differences in their models, and asked one another questions. The technology supported conversation and collaboration, helping create a sense of community rather than isolated problem-solving.

By the end of the workshop, students seemed more confident and engaged. The combination of visualization, structured note-taking, and peer sharing helped transform a challenging topic into something accessible and meaningful.

Experiences like this remind me that when students can see mathematics, talk about it, and learn together, calculus becomes far less intimidating and far more powerful.

As a calculus instructor, one thing I’ve noticed year after year is that students don’t struggle with calculus because they’re incapable.

They struggle because calculus is often introduced as a list of procedures rather than as a way of thinking.

In many first-year courses, students quickly become focused on rules: differentiate this, integrate that, memorize formulas, repeat steps. And while procedural fluency is certainly part of learning mathematics, I’ve found that this approach can sometimes come at the cost of deeper understanding.

Students begin to feel that calculus is something to survive, rather than something to make sense of.

Research supports this concern when calculus becomes overly mechanical; students often miss the conceptual meaning behind the mathematics. That realization has pushed me to reflect more carefully on what I want students to take away from my class.

Over time, I’ve become increasingly interested in teaching approaches that emphasize mathematical thinking, not just computation.

Thinking Beyond Formulas

When I teach calculus, I want students to ask questions that go beyond getting the right answer:

1. What does this derivative actually represent?
2. How does the function behave when something changes?
3. Why do certain patterns keep appearing again and again?

These kinds of questions are often where real learning begins.

In The Role of Maple Learn in Teaching and Learning Calculus Through Mathematical Thinking, mathematical thinking is described through three key processes:

1. Specializing - exploring specific examples
2. Conjecturing - noticing patterns and testing ideas
3. Generalizing - extending those patterns into broader principles

This framework captures the kind of reasoning I hope students develop as they move through calculus.

What Helps Students See the Mathematics

One of the biggest challenges in teaching calculus is helping students see the mathematics, not just perform it.

It’s easy for students to get stuck in algebraic steps before they ever have the chance to build intuition. I’ve found that students learn more effectively when they can explore examples, visualize behavior, and experiment with ideas early on.

Sometimes that happens through discussion, sometimes through carefully chosen problems, and sometimes through interactive tools that allow students to test patterns quickly.

The goal isn’t to replace thinking it’s to support it.

A Meaningful Example

One activity highlighted in the study, Inflation and Time Travel, places exponential growth into a context students can relate to: wages and inflation.

When students adjust values, observe trends, and ask what happens over long periods of time, calculus becomes much more than an abstract requirement. It becomes a way of understanding real phenomena.

Activities like this remind students that mathematics is not just symbolic work on paper; it is a way of describing and interpreting the world.

Final Thoughts

For me, calculus is not meant to be a barrier course.

It’s meant to be a gateway into powerful ways of reasoning about change, structure, and patterns.

When students begin to specialize, make conjectures, and generalize ideas for themselves, they start to experience calculus as something meaningful, not just mechanical.

And as an instructor, that is exactly what I hope to cultivate in my classroom.

If one of our posts showed up in your social media feed recently, you may have found yourself staring at a giant maple leaf with feet and thinking, “Wait… who (or what) is that?” you’re not alone. 

Yes, that big, cheerful leaf you’ve been seeing is very real. 
And yes, they have a name. 

Meet Mathy. 

We officially introduced Mathy to the world a couple of weeks ago at JMM 2026 in Washington, DC, but their story actually started much earlier. 

Mathy was originally created by one of our developers, Marek Krzeminski, a few years ago as a fun internal character. Over time, they quietly became our in-office, local mathscot, popping up as mini 3D-printed Mathys around the office and even as a custom emoji someone created. 

Then, sometime last year, someone had what can only be described as a bold idea: 

What if we brought Mathy to life? 

And just like that, the giant maple leaf went from concept to costume. 

Mathy is fun, curious, and a little playful. That’s very intentional. That’s what math should feel like. 

We believe math matters. We also believe math should be approachable, joyful, and a place where curiosity is rewarded. Mathy reminds us, and hopefully others, that math doesn’t have to be intimidating. It can be fun, and it can inspire awe. 

I’ll be honest. When we decided to bring Mathy to JMM, I was a little nervous. Conferences are busy, serious places. Would people really want to interact with a seven-foot-tall maple leaf? 

As it turns out, yes. Very much yes. 

Researchers (from postdocs to seasoned academics), educators, and undergraduate and graduate students all stopped, smiled, laughed, and asked for photos. At one point, people were actually lining up to take pictures with Mathy.

Let’s just say: Mathy was a hit. 

How tall is Mathy? 
About 7 feet. They are hard to miss. 

What does Mathy love (besides math)? 
Dancing. Very much dancing. 
You can see for yourself here: Mathy's got moves!

Does Mathy talk? 
You bet they do. 

Now that Mathy has officially been introduced to the world, you’ll be seeing them more often on social media, at events, and in a few other fun places we’re cooking up. 

So if you spot a giant maple leaf dancing, waving, or talking math, now you know who they are. 

If you spot Mathy, don’t be shy, say hi. 

Many problems in mathematics are easy to define and conceptualize, but take a bit of deeper thinking to actually solve. Check out the Olympiad-style question (from this link) below:

Former Maplesoft co-op student Callum Laverance decided to make a document in Maple Learn to de-bunk this innocent-looking problem and used the powerful tools within Maple Learn to show step-by-step how to think of this problem. The first step, I recommend, would be to play around with possible values of a and b for inspiration. See how I did this below:

Based on the snippet above, we might guess that a = 0.5 and b = 1.9. The next step is to think of some equations that may be useful to help us actually solve for these values. Since the square has a side length of 4, we know its area must be 4² = 16. Therefore, the Yellow, Green and Red areas must add exactly to 16. That is,

With a bit of calculus and Maple Learn's context panel, we can integrate the function f(x) = ax² from x = -2 to x = 2 and set it equal to this value of 8/3. This allows us to solve for the value of a.

We see that a = 1/2. Since the area of the Red section must be three times that of the Yellow (which we determined above to be 8/3), we get Red = (8/3)*3 = 8.

The last step is to find the value of b. In the figure below, we know that the line y = 4 and the curve y = bx² intersect when bx² = 4 (i.e. when x = ± 2/sqrt(b)).

Since we know the area of the red section is 8 square units, that must be the difference between the entire area underneath the horiztonal line at y = 4 and the curve y = bx² on the interval [-2/sqrt(b), 2/sqrt(b)]. We can then write the area of the Red section as an integral in terms of b, then solve for the value of b, since we know the Red area is equal to 8.

Voila! Setting a = 1/2 and b = 16/9 ≈ 1.8 guarantees that the ratio of Yellow to Green to Red area within the square is 1:2:3, respectively. Note this is quite close to our original guess of a = 0.5 and b = 1.9. With a bit of algebra and solving a couple of integrals, we were able to solve a mathematics Olympiad problem!

The inscribed square problem, also known as the Toeplitz conjecture, is an unsolved quastion in geometry: Does every plane simple closed curve (Jordan curve) contain all four vertices of some square? This is true if the curve is convex or piecewise smooth and in other special cases. The problem was proposed by Otto Toeplitz in 1911. For detailes see  https://en.wikipedia.org/wiki/Inscribed_square_problem

The Inscribed_Square procedure finds numerically one or more solutions for a curve defined by parametric equations of its boundary or by the equation F(x,y)=0. The required parameter of procedure  L  is the list of equations of the boundary links or the equation  F(x,y)=0 . Optional parameters:  N  and  R . By default  N='onesolution' (the procedure finds one solution), if  N  is any symbol (for example  N='s'), then more solutions.  R  is the range for the length of the side of the square (by defalt  R=0.1..100 ).

The second procedure  Pic  visualizes the results obtained.

The codes of the procedures:

restart;
Inscribed_Square:=proc(L::{list(list),`=`},N::symbol:='onesolution',R::range:=0.1..100)
local D, n, c, L1, L2, L3, f, L0, i, j, k, m, A, B, C, P, M, eq1, eq2, eq3, eq4, eq5, eq6, eq7, eq8, eq9, sol, Sol;
uses LinearAlgebra;
if L::list then
L0:=map(p->`if`(type(p,listlist),[[p[1,1]+t*(p[2]-p[1])[1],p[1,2]+t*(p[2]-p[1])[2]],t=0..1],p), L);
c:=0;
n:=nops(L);
for i from 1 to n do
for j from i to n do
for k from j to n do
for m from k to n do
A:=convert(subs(t=t1,L0[i,1]),Vector): 
B:=convert(subs(t=t2,L0[j,1]),Vector):
C:=convert(subs(t=t3,L0[k,1]),Vector): 
D:=convert(subs(t=t4,L0[m,1]),Vector):
M:=<0,-1;1,0>;
eq1:=eval(C[1])=eval((B+M.(B-A))[1]);
eq2:=eval(C[2])=eval((B+M.(B-A))[2]);
eq3:=eval(D[1])=eval((C+M.(C-B))[1]);
eq4:=eval(D[2])=eval((C+M.(C-B))[2]);
eq5:=eval(DotProduct(B-A,B-A, conjugate=false))=d^2;
sol:=fsolve([eq1,eq2,eq3,eq4,eq5],{t1=op([2,2,1],L0[i])..op([2,2,2],L0[i]),t2=op([2,2,1],L0[j])..op([2,2,2],L0[j]),t3=op([2,2,1],L0[k])..op([2,2,2],L0[k]),t4=op([2,2,1],L0[m])..op([2,2,2],L0[m]),d=R});
if type(sol,set(`=`)) then if N='onesolution' then return convert~(eval([A,B,C,D],sol),list) else c:=c+1; Sol[c]:=convert~(eval([A,B,C,D],sol),list) fi;
 fi; 
od: od: od: od:
Sol:=fnormal(convert(Sol,list),7);
print(Sol);
ListTools:-Categorize((X,Y)->`and`(seq(is(convert(X,set)[i]=convert(Y,set)[i]),i=1..4)) , Sol);
return map(t->t[1],[%]);
else
A,B,C,D:=<x1,y1>,<x2,y2>,<x3,y3>,<x4,y4>:
M:=<0,-1;1,0>:
eq1:=eval(C[1])=eval((B+M.(B-A))[1]):
eq2:=eval(C[2])=eval((B+M.(B-A))[2]):
eq3:=eval(D[1])=eval((C+M.(C-B))[1]):
eq4:=eval(D[2])=eval((C+M.(C-B))[2]):
eq5:=eval(LinearAlgebra:-DotProduct((B-A,B-A), conjugate=false))=d^2:
eq6:=eval(L,[x=x1,y=y1]):
eq7:=eval(L,[x=x2,y=y2]):
eq8:=eval(L,[x=x3,y=y3]):
eq9:=eval(L,[x=x4,y=y4]):
sol:=fsolve({eq1,eq2,eq3,eq4,eq5,eq6,eq7,eq8,eq9},{seq([x||i=-2..2,y||i=-2..2][],i=1..4),d=R});
eval([[x1,y1],[x2,y2],[x3,y3],[x4,y4]], sol):
fi;
end proc:

Pic:=proc(L,Sol,R::range:=-20..20)
local P1, P2, P3, T;
uses plots, plottools;
P1:=`if`(L::list,seq(`if`(type(s,listlist),line(s[],color=blue, thickness=2),plot([s[1][],s[2]],color=blue, thickness=2)),s=L), implicitplot(L, x=R,y=R, color=blue, thickness=2, gridrefine=3));
P2:=polygon(Sol,color=yellow,thickness=0);
P3:=curve([Sol[],Sol[1]],color=red,thickness=3):
T:=textplot([[Sol[1][],"A"],[Sol[2][],"B"],[Sol[3][],"C"],[Sol[4][],"D"]], font=[times,18], align=[left,above]);
display(P1,P2,P3,T, scaling=constrained, size=[800,500], axes=none);
end proc:

Examples of use:

The curve consists of a semicircle, a segment and a semi-ellipse (find 1 solution):

L:=[[[cos(t),sin(t)],t=0..Pi],[[t,0],t=-1..0],[[0.5+0.5*cos(t),0.8*sin(t)],t=Pi..2*Pi]]:
Sol:=Inscribed_Square(L);
Pic(L,Sol);

The procedure finds 6 solutions for a non-convex pentagon:

 L:=[[[0,0],[9,0]],[[9,0],[8,5]],[[8,5],[5,3]],[[5,3],[0,4]],[[0,4],[0,0]]]:
Sol:=Inscribed_Square(L,'s');
plots:-display(Matrix(3,2,[seq(Pic(L,Sol[i]),i=1..6)]),size=[300,200]);

For an implicitly defined curve, only one solution can be found:

L:=abs(x)+2*abs(y)-sin((2*x-y))-cos(x+y)^2=3:
Sol:=Inscribed_Square(L);
Pic(L,Sol);

               
See more examples in the attached file.

Inscribed_Square.mw

Over the past year, I have spent a lot of time talking to educators, researchers, and engineers about AI. The feeling is almost universal: it is impressive, it is helpful, but you should absolutely not trust it with your math even if it sounds confident.

That tension between how capable AI feels and how accurate it actually is has been on my mind for months. AI is not going away. The challenge now is figuring out how to make it reliable.

That is where Maple MCP comes in.

Maple MCP (Model Context Protocol) connects large language models like ChatGPT, Claude, Cohere, and Perplexity to Maple’s world-class math engine.

When your AI encounters math, your AI can turn to Maple to handle the computation so the results are ones you can actually trust.

It is a simple idea, but an important one: Maple does the math and the AI does the talking. Instead of guessing, the AI can be directed to call on Maple whenever accuracy matters.

Model Context Protocol (MCP) is an emerging open standard that allows AI systems to connect to external tools and data sources. It gives language models a structured way to request computations, pass inputs, and receive reliable outputs, rather than trying to predict everything in text form.

Here is a high-level view of how MCP fits into the broader ecosystem:

MCP lets an AI system connect securely to specialized services, like Maple, that provide capabilities the model does not have on its own.

If you want to learn more about the MCP standard, the documentation is a great starting point: Model Context Protocol documentation

Here is a glimpse of what happens when Maple joins the conversation:

Depending on the prompt, Maple MCP can evaluate expressions symbolically or numerically, execute Maple code, expand or factor expressions, integrate or solve equations, and even generate interactive visualizations. If you ask for an exploration or an activity, it can create a Maple Learn document with the parameters and sliders already in place.

As an example of how this plays out in practice, I asked Maple MCP:

“I'd like to create an interactive math activity in Maple that allows my students to explore the tangent of a line for the function f(x) = sin(x) + 0.5x for various values of x.”

It generated a complete Maple Learn activity that was ready to use and share. You can open the interactive version here: interactive tangent line activity .

In full disclosure, I did have to go back and forth a bit to get the exact results I wanted, mostly because my prompt wasn’t very specific, but the process was smooth, and I know it will only get better over time.

What is exciting is that this does not replace the LLM; it complements it. The model still explains, reasons, and interacts naturally. Maple simply steps in to do the math—the part AI cannot reliably do on its own.

We have opened the Maple MCP public beta, and I would love for you to try it.

Sign up today and we will send you everything you need to get started!

The Autumn Issue is now up, at mapletransactions.org

This issue contains two Featured Contributions; a short but very interesting one by Gilbert Labelle on a topic very dear to my own heart, and a longer and also very interesting one by Wadim Zudilin.  I asked Doron Zeilberger about Wadim's paper, and he said "this is a true gem with lots of insight and making connections between different approaches."

The "Editor's Corner" paper is a little different, this time.  This paper is largely the work of my co-author, Michelle Hatzel, extracted and revised from her Masters' thesis which she defended successfully this past August.  I hope that you find it as interesting as I did.

We have three refereed contributions, a contribution on the use of Maple Learn in teaching, and a little note on my design of the 2026 Calendar for my upcoming SIAM book with Nic Fillion, as well.  All the images for the calendar were generated in Maple (as were most of the images in the book).

It's been fun to put this issue together (with an enormous amount of help from Michelle) and I hope that you enjoy reading it.

I would also like to thank the Associate Editors who handled the refereeing: Dhavide Aruliah, David Jeffrey, and Viktor Levandovskyy.

It is possible to construct a system of equations that defines all spheres that touch two smooth surfaces.

This includes all the spheres inscribed between these surfaces. The first two equations are surfaces,  x7, x8, x9 are the coordinates of the centers of the spheres, the remaining variables correspond to the coordinates of the points of contact. 
In the program, we inscribe a sphere between a cylinder and a sphere. An example of obtaining one of the infinite subsets of the solution to a system of equations is implemented. This solution is "responsible" for the equation f8.
INSCRIBED_SPHERES_CILL_FOR.mw

And one more example.

The recordings from Maple Conference presentations, including the workshops, are now available on the conference website.

Thank you to all those who attended or presented, you made the conference a great success!
We hope to see you all again next year.

Kaska Kowalska
Contributed Program Co-Chair

There is still time to register for Maple Conference 2025, which takes place November 5-7, 2025.

The free registration includes access to three full days of presentations from Maplesoft product directors and developers, two distinguished keynote speakers, contributed talks by Maple users, and opportunities to network with fellow users, researchers, and Maplesoft staff.

The final day of the conference will feature three in-depth workshops presented by the R&D team. You'll get hands-on experience with creating professional documents in Maple, learn how to solve various differential equations more effectively using Maple's numerical solvers, and explore the power of the Maple programming language while solving interesting puzzles.

Access to the workshops is included with the free conference registration.

We hope to see you there!

Kaska Kowalska
Contributed Program Co-chair

Imagine standing 365 metres above Toronto on the CN Tower’s EdgeWalk and throwing a baseball. Could you actually land it on third base at Rogers Centre, about 263 metres away?

Sportsnet raised this question, and we decided to put it to the test in Maple Learn, check out this document to see the answer.

Also take a look at the Sportsnet video on the problem, to see why the answer may not be obvious.

In the Maple Learn document, you can adjust the initial speed and angle at which to throw the ball and then visualize its trajectory (without having to throw as hard as Addison Barger).

I was surprised that even in the simplified projectile motion model, that neglects air resistance, AND assuming I could throw at 60mph (a questionable assumption to say the least) I wouldn’t be able to hit the base myself.

I then used Maple to build a more realistic model that would account for air resistance. The equations below model the position of the ball, where y(0) = h₀ is the initial height of 365m and v₀ is the initial speed.

local h0, m, d, rho, g:
	h0 := 365:
	m := 0.145:
	d := 0.072:
	rho := 1.225:
	g := 9.81:

	local eqns, ics:
	eqns := diff(x(t),t) = u(t), 
		    diff(y(t), t) = v(t), 
		    diff(u(t), t)= -Pi/16 * d^2 * rho/m * sqrt(u(t)^2 + v(t)^2) * u(t), 
		    diff(v(t), t)= - g - Pi/16 * d^2 * rho/m * sqrt(u(t)^2 + v(t)^2) * v(t):
	ics := x(0) = 0, y(0)=h0, u(0) = v_initial*cos(theta_initial), v(0) = v_initial * sin(theta_initial):

	local ans, xpos, ypos:
	ans := dsolve([eqns, ics], numeric, output=listprocedure):
	xpos := subs(ans, x(t));
	ypos := subs(ans, y(t));

In the Maple Learn document, you can visualize the difference between the models by comparing the trajectories. The trajectory from the simple model is shown in blue, and the trajectory after accounting for air resistance is modelled in red.

Accounting for air resistance, I’m no longer convinced even Addison Barger could accomplish this challenge.

Check out the Maple Learn document to try for yourself!

The full program for Maple Conference 2025 is now available. 

The agenda includes two full days of keynote speakers, presentations from Maplesoft product directors and developers, and contributed talks by Maple users all around the world. There will be opportunities to network with fellow users, researchers, and Maplesoft staff.

The final day of the conference will include three in-depth workshops presented by the R&D team.
The workshops will explore how to:

• Create papers and reports in Maple
• Solve various differential equations more effectively using Maple's numerical solvers
• Solve Advent of Code challenges using Maple

Access to the workshops is included with the free conference registration.

We hope to see you there!

Kaska Kowalska
Program Co-chair

When we think about AI, most of us picture tools like ChatGPT or Gemini. However, the reality is that AI is already built into the tools we use every day, even something as familiar as a web search. And if AI is everywhere, then so are its mistakes.

A Surprising Answer from Google

Recently, I was talking with my colleague Paulina, Senior Architect at Maplesoft, who also manages the team that creates all the Maple Learn content. We were talking about Google’s AI Overview, and I said I liked it because it usually seemed accurate. She disagreed, saying she’d found plenty of errors. Naturally, I asked for an example.

Her suggestion was simple: search “is x + y a polynomial.”

So I did. Here’s what Google’s AI Overview told me:

“No, x + y is not a polynomial”

My reaction? HUH?!

The explanation correctly defined what a polynomial is but still failed to recognize that both x and y each have an implicit exponent of 1. The logic was there, but the conclusion was wrong.

Using It in the Classroom

This makes a great classroom example because it’s quick and engaging. Ask your students first whether x + y is a polynomial, then show them the AI result. The surprise sparks discussion: why does the explanation sound right but end with the wrong conclusion?

In just a few minutes, you’ve not only reviewed a basic concept but also reinforced the habit of questioning answers even when they look authoritative.

Why This Matters

As I said in a previous post, the real issue isn’t the math slip, it’s the habit of accepting answers without questioning them. It’s our responsibility to teach students how to use these tools responsibly, especially as AI use continues to grow. Critical thinking has always mattered, and now it’s essential.

Hi again all,

Was trying to be helpful at

mathforums.com

and made these two Maple files.  

simple_square_root_loop.mw

simple_square_root_loop.pdf

Hope that helps.

Maple is the best :-)

goodbye for now.

Matthew