#coursera

On June 29, I had the opportunity to speak with the incoming MBA batch at NMIMS Mumbai during the launch of NMIMS Horizon in partnership with Coursera.

It was a good moment to speak to students who are just starting their MBA journey. They are entering a program that will shape how they think, solve problems, work with others, and prepare for the next phase of their careers. At the same time, they'll be entering a workplace that is changing faster than most of us have seen before.

The wanted to leave them with a simple point. Do not start by trying to become an AI expert. Start by becoming an AI-powered learner.

That distinction matters. An AI expert needs depth in models, systems, tools, infrastructure, and implementation. That is important work, but it is not where every student needs to begin. An AI powered learner is someone who knows how to learn faster, ask better questions, use the right resources at the right time, and keep adapting as the world changes.

That is a much more practical starting point.

The telephone and cars took decades to reach mass adoption. ChatGPT reached 100 million users in just two months. That is not just an interesting technology statistic. It tells us something important about the world students are preparing for. New tools and new ways of working will reach them faster than ever before.

The World Economic Forum estimates that 39% of key skills will change in the next five years. By 2030, 40% of core job skills are expected to shift. Employers are increasingly looking at micro-credentials as part of the hiring signal, and job postings mentioning GenAI skills grew 4x in 2024.

For MBA students, this is not abstract. Whether they go into consulting, finance, marketing, operations, product, HR, analytics, entrepreneurship, or roles that do not exist yet, AI will be part of their working environment. It may not replace everything they do, but it will change what good work looks like.

When everyone has access to similar tools, the advantage will not come from simply knowing which button to click. The advantage will come from judgment. Can you frame the problem well? Can you ask sharper questions? Can you connect data, customer context, business goals, domain knowledge and execution? Can you communicate clearly? Can you move from idea to action?

That is why learning how to learn is not a soft skill anymore. It is a career skill.

The practical advice I shared with students was intentionally simple. Start small and start today.

None of this needs to be dramatic. Capability is built through repetition. A student who uses AI once as a shortcut may not learn much. A student who uses it regularly to question, revise, practice, test understanding, and improve their work will build a very different kind of confidence.

That is also where platforms like Coursera can be powerful. Access to high-quality content matters, but access alone is not the outcome. The outcome comes from how students use that access. Do they go deeper? Do they practice? Do they build proof of skill? Do they use tools like Coach, Dialogues, and Role Plays to make learning more active and applied?

This is the opportunity with NMIMS Horizon. It is not just about adding more online courses to an MBA program. It is about helping students build a more adaptive learning rhythm while they are still in the classroom, so they are better prepared when they enter the workplace.

What I enjoyed about the session was the timing. These students are at the beginning of their MBA journey. They do not need to have every answer today. In fact, they should not expect to. But they do need to build the habit of staying curious, experimenting with new tools, and using learning as a way to stay ready.

The future of work will keep changing. The specific tools will change. Markets will change. Roles will change. The students who build the discipline to keep learning will always have a way forward.

🎓 Excited to share that I've successfully completed the "Work Smarter with Microsoft Word" course, authorized by Microsoft and offered through Coursera!

This course helped me level up my productivity skills and learn how to use Microsoft Word more efficiently and professionally. 💼✨

📌 Issued by: Microsoft x Coursera 📅 Date: June 13, 2026

Never stop learning! 🚀

https://coursera.org/verify/JBIZGM254XOD

Selected Dataset For this assignment, I have selected the Mars Crater Database developed by Stuart Robbins.

This dataset contains a large-scale global inventory of Mars craters, including variables such as:

Latitude and longitude of crater location Diameter and depth measurements Ejecta morphology classifications Number of identifiable crater layers The dataset is statistically complete for craters with diameter ≥ 1 km, making it suitable for quantitative analysis. [Mars Crater Codebook | PDF]

Topic of Interest (Step 2) My primary topic of interest is:

➡️ Crater size and structure

Specifically:

Crater diameter (DIAM_CIRCLE_IMAGE) Crater depth (DEPTH_RIMFLOOR_TOPOG) These variables describe the physical geometry of craters and provide insight into crater formation processes.

Initial Research Question + Hypothesis ✅ Research Question: Is crater diameter associated with crater depth on Mars?

✅ Hypothesis: Larger craters are expected to have greater depths, but the relationship may not be strictly linear due to erosion, geological modification, and collapse processes.

Personal Codebook (Step 3) Variable Name

Description

Units

CRATER_ID

Unique crater identifier

N/A

LATITUDE_CIRCLE_IMAGE

Latitude of crater centre

Degrees

LONGITUDE_CIRCLE_IMAGE

Longitude of crater centre

Degrees

DIAM_CIRCLE_IMAGE

Diameter of crater

km

DEPTH_RIMFLOOR_TOPOG

Depth from rim to floor

km

All variables are directly defined in the codebook you provided. [Mars Crater Codebook | PDF]

Second Topic of Interest (Step 4) After reviewing the dataset further, I identified a second topic:

➡️ Crater morphology and layering structure

Relevant variables:

MORPHOLOGY_EJECTA_1 / 2 / 3 NUMBER_LAYERS These describe:

Ejecta patterns (e.g., lobes, textures) Structural layering around craters

Expanded Research Question Is crater size (diameter) associated with the number of ejecta layers observed?

Updated Hypothesis Larger craters are more likely to have multiple ejecta layers due to higher impact energy and more complex material displacement.

Updated Personal Codebook (Step 5) Variable Name

Description

Units

DIAM_CIRCLE_IMAGE

Diameter of crater

km

DEPTH_RIMFLOOR_TOPOG

Crater depth

km

MORPHOLOGY_EJECTA_1

Ejecta type classification

Categorical

MORPHOLOGY_EJECTA_2

Ejecta morphology structure

Categorical

MORPHOLOGY_EJECTA_3

Texture/shape classification

Categorical

NUMBER_LAYERS

Number of ejecta layers

Count

Literature Review (Step 6) ✅ Search Terms Used: “Mars crater diameter and depth relationship” “impact crater morphology Mars layers ejecta” “crater scaling laws planetary surfaces” ✅ Key References: Robbins, S.J. (2011). Planetary Surface Properties, Cratering Physics, and the Volcanic History of Mars Melosh, H.J. (1989). Impact Cratering: A Geologic Process Pike, R.J. (1974). “Depth/Diameter relations of Mars craters” Boyce, J. (2008). The Craters of Mars

Summary of Findings (Step 6 – Required) From the literature:

Crater depth increases with diameter, but not proportionally (non-linear scaling). Larger craters often undergo collapse, reducing depth relative to size. Complex craters exhibit: Central peaks Multiple ejecta layers Ejecta morphology depends on: Impact energy Surface composition (ice, dust, rock) Overall pattern: ➡️ Size influences both depth and morphological complexity

✅ Final Hypothesis (Step 7) There is a positive relationship between crater diameter and crater depth, and larger craters are associated with a higher number of ejecta layers due to increased impact energy and structural complexity.

Last month, the Coursera team spent a day in Bhubaneswar with 100+ faculty members from Biju Patnaik University of Technology, Odisha, for a Faculty Development Program on "Strategic Thinking, Smarter Teaching & AI in the Classroom." The session was held in partnership with Odisha Skill Development Authority.

I have been part of many conversations on AI and education over the last couple of years. Some are exciting, some are speculative, and some get too abstract very quickly. What stood out at this event was how practical the discussion was.

Faculty were not asking whether AI is coming into the classroom. That question is already behind us. They were asking better questions.

How can AI help me teach more effectively?

How should assessments change when students have access to powerful tools?

How do we help students build skills that matter outside the classroom?

How do we use technology without losing the human judgment that good teaching needs?

That tone made the day valuable.

During the fireside chat on the future of work, one point came through clearly: industry expectations are changing quickly. Students will need more than subject knowledge. They will need adaptability, confidence with new tools, problem-solving ability, and hands-on experience applying what they learn. This is easy to say and hard to build into an academic system.

That is where faculty play such an important role.

Technology can support learning, but it does not automatically create better learning. A platform can make content available. AI can help generate ideas, simulate practice, or personalize support. But the design of the learning experience still matters. The teacher still matters. The context still matters.

I had the opportunity to showcase some of Coursera's AI capabilities, including Course Builder, Coach, Role Plays, Dialogues, and AI-powered learning paths. What I tried to focus on was not the novelty of the tools, but the teaching problems they can help solve.

Course Builder can help faculty move from idea to structured learning experience faster.

Coach can give learners support when they get stuck.

Role Plays and Dialogues can create safe practice environments where students can build confidence before they face real situations.

AI-powered learning paths can help connect skills, goals, and content more thoughtfully.

None of this replaces the faculty member. If anything, it makes the faculty role more important. Someone still needs to decide what good learning looks like, what students should practice, how feedback should be used, and how the experience connects to the real world.

The questions from faculty were thoughtful and encouraging. You could sense curiosity, but also healthy caution. That balance is important. Education should not adopt every new tool just because it is available. At the same time, institutions cannot afford to ignore a shift that is already changing how students learn and how work gets done.

What encouraged me most was the openness to experiment. Not in a reckless way, but in a practical way. Faculty wanted to understand what these tools can do, where they can help, and where human guidance still needs to lead.

Days like this make the future of education feel less like a headline and more like real work.

The shift is already underway. The next phase is about building momentum in the right direction: better teaching, more applied learning, smarter assessments, and stronger connections between classroom learning and the skills students will need after graduation.

Big thanks to Prof. Amiya Kumar Rath, Prof. Sujit Kumar Khuntia, and the BPUT team for partnering with us. Grateful to Dr. Neharika Vohra and Deepak Kumar Arora for bringing depth and perspective to the conversations. And thank you to the Coursera team, both at the event and behind the scenes, for making the day successful.

There is a lot still to figure out. But after spending a day with faculty who are curious, serious, and willing to engage with the hard questions, I left optimistic.