SEED: Understanding the warning triggers for failures

I was facilitating a leadership course focusing on organizational transformation at Northeastern University! Learners came from Human Resources, Project Management, Informatics, and Leadership concentrations. In one of the discussions related to reasons for organizational failures, learners discussed initiatives failed because because of poor market research, management myopia, process overhead, ethical oversight and overreliance on technology instead of people. When I asked a few follow-up questions related to the reason for these failures, everyone narrowed in on bad strategy! 

How could strategy by itself fail? After all, strategy is to create a "competitive advantage" and failure lies in people not paying attention to the warning triggers and make the required course corrections! So, I explained to the learners the thoughts behind what I call as SEED warning triggers. This is an approach I developed over a period when managing the Program Management Office working with C-Suite directly and clients delivering numerous programs supporting a few portfolios. 

Success Measure: An organization is like a big family taking care of members of the family. Some family members may be children while others adults. What makes one happy may not make others happy as everyone has specific goals and objectives. If organization is a family, every member's longer term needs are like the organizational initiatives.  Most initiatives focus on achieving specific goals and objectives. So, this is frequently one of the things people identify in various documents, such as the business case, benefit management plan, benefit register, project charter, etc. Some metric driven organizations may have specific objectives & key results (OKR) to evaluate interim progress as well that are used in the governance framework to prioritize and optimize initiatives aligned to the enterprise environmental factors and related resource management needs as well.

Entry Consideration:  An organization is a living entity with every portfolio, program, and product a healthy organ working holistically to sustain life. It is therefore important to have interim reviews to evaluate the level of success achieved until then and reevaluate what initiatives should continue moving forward. Even a successful initiative that has met all the criteria may be parked for a different initiative because of the external and internal events. So, it is always better to have predefined milestones (it could be functionality or schedule based) to evaluate if the initiative should continue to enter its next phase based on its own success criteria met. 

Exit Criteria: One of the major challenges with any initiative is failing to understand, recognize, and act on exit criteria. In my humble opinion, this is one of the main warning triggers people ignore. If one's lifestyle changes causes an unhealthy situation (e.g.: prolonger exposure to construction work causes hearing loss, longer commutes to work causing family imbalance, toxic work environment causing increased stress), then, one has to make lifestyle changes which may sometimes involve looking for options (e.g.: a different role in the company, remote work options, an alternative job) to exit from the current challenges. Similar logic applies in a portfolio, program, or project where we should first have the exit criteria and monitor the warning signs to determine when it is time to "STOP." Continuing to do the same thing expecting different results is not smartness!

Decision Delay: This is the final and more challenging issue. Even when one knows that the heredity may have possible diabetes, people follow specific lifestyle (eating sugary foods) leading to diabetes diagnosis. Despite the diagnosis, they don't exit the current lifestyle options creating more challenges for their family members. Their reason is delaying the decision to act on the earlier warning signals. This delayed decision-making is the cancer that kills all the initiatives. When products delay customers' requests for newer functionality for a long time, for example, customers are dissatisfied (relate to Kano model here). When portfolios defer rebalancing the initiatives or reskill the employees and programs delegate program level risks constituent project sustaining benefits, such delays seal the failure of any initiative. This is where the skilled and timely governance really matters!

So, in the end, my SEED triggers are example of things that leaders should constantly monitor and manage. Otherwise, failures are just accidents waiting to happen. I felt some learners in the class felt thrilled to learn about this experiential approach. What are your thoughts? Please share.

The science of estimation is an art rooted in risk management

I frequently find people mixing up estimation techniques between plan-driven and change-driven approaches thinking they are all completely different. While there are small changes, the art of estimation is simply looking at the level of accuracy when estimated and the extent of confidence in the given estimate for working on it! From that angle, the approaches like the analogous estimation, parametric estimation, triangular estimation, the special PERT estimation, affinity estimation, relative sizing, and storypointing can all be categoized into top-down, budget, and bottom-up estimation. 

Top Down Estimation

• The top-down estimate is often based on gut feel. It draws on the experience of previous projects (hence analogous). It could be driven off of someone's expertise (expert judgment) or based on knowledge tracked in historical records (corporate knowledge base). It is frequently done at the early stages of a project (initiation) where minimal effort is required to get a feel for whether an initiative should be undertaken or not! As a result, the level of accuracy is very low (-25% to +75%). This is why this technique is called rough order of magnitude (ROM) or order of magnitude (OOM). 
• In adapative projects where features representing a collection of stories and tasks not yet broken down are estimated, these collection of work closer to each other (hence affinity) are estimated in abstaction units such as T-shirt or Coffee-cup sizes. Hence such estimates are called affinity estimates in the backlog!

Budget Estimation

• As the project continues with planning, we look at increasing our confidence in the estimate. So, we get down to decomposing the details, such as the parameters required to estimate or seek opinion from multiple experts to narrow our estimate. Since we apply the parameters (number of rooms to paint * amount of paint required * price per paint can; number of virtual machines on the cloud * number of active hours * price per hour), we call it parametric estimation. The details of the parameters applied vary based on the industry and the project. 
• When it comes to seeking expert's opinion, instead of seeking estimate from estimate alone, we seek 3-points (optimistic, most likely, and pessimistic) to get the average. This average applies the statistical principle of central tendency to even out the variations. The same logic applies with PERT (program evaluation review technique) where multiple points are observed where the princple of normalization (bell curve logic) can be expected. So, PERT becomes a special case of triangular estimate. 
• For adaptive projects, the parametric thoughts carry forward. So, a team looks at a feature in relation to another feature (either delivered or to be delivered). So, if the new feature is twice the size of another feature which is either of small T-shirt size or 3 points, then, the new feature is either medium size or 8 points. These estimates may apply at the release level backlog giving the definition of ready (DoR) following the DEEP (Detailed Appropriately, Estimable, Emergent, Prioritized) property.
• Naturally, there is more time required to identify the parameters, seek opinions from multiple people, and perform this estimation. As the level of acuracy increases between -10% to +25%, the duration taken to estimate also increases.

Bottom Up Estimation

• When the project continues in its later stages of planning, then, increased level of accuracy and confidence are required to allocate work to someone and spend the money. Consequently, teams engage in more granular breakdown of tasks (activities) or modules (function points) so that these activities can be estimated, dependencies understood, and lead/lag factored appropriately to compute the 'definitive' estimate.
• In the case of adaptive projects, here is where the features or user stories are further broken following the INVEST (independent, negotiable, valuable, estimable, small, testable) property so that they are good candidates for sprint/iteration planning. In the sprint planning, the team does the cumulative estimate applying the central tendency to arrive at the stroy point (hence story pointing). The planning poker player at this stage is to faciliate the team level estimate as the expectation at the scrum level is that anyone should be able to pick up any story (cross-functional, multidisciplinary, T-shaped skills based, self-managed team). As work is constantly refined through backlog refinement, release level planning, and iteration level planning, the agile teams gain significant efficiency in sprint planning as the time taken to do this estimation is spread over time. 

Figure: Estimation Illustration by Sriram Rajagopalan

As you can see, the entire art of estimation is a classic risk management exercise where not only the management stakeholders but also the delivery team converge on the constraints, assumptions, risks, and dependencies (that I call as the CARD of the business goals and objectives). No wonder project management is not only a science but an art!

Thoughts? What do you think? 

ALM 3.0: Emerging role of the Modern Cost of Quality guiding the decision-making in the Human–AI ecosystem

For about a couple of years, I have been rethinking about the emergence of the modern cost of quality. Rooted in the total quality management principles, the cost of quality grouped prevention and appraisal costs under the cost of conformance and internal and external failure under the cost of non-conformance    (Boehm, 1981). I still believe that these approaches are very valid today but need to recognize reflective leadership (Rajagopalan, 2018) for additional layers of technology convergence (5th Industrial Revolution is already here) (Anil, 2025), strategic decision-making (value delivery is much more than product features) (Rothaermel, 2019), and the ethical decision-making (Trevino & Nelson, 2017; Canca, 2020; Kemell & Vakkuri, 2024) in products and tools (AI considerations like beneficence, non-maleficence, justice, autonomy) used in today's workforce as well as their impact on the customers (Rajagopalan, 2015) these products and tools serve. 

One of the premise behind my development of the modern cost of quality is that quality itself is no longer becoming an outcome of what is delivered. It is part of a continuously governed decision space! However, in practice people used application lifecycle management (ALM) tools in their plan-driven and change-driven approaches as a repository of artifacts to provide traceability and auditability in their compliance ecosystem! My synthesis of some observations are as follows:

• Decisions are more important than artifacts
• Quality is multidimensional and begins with needs assessment
• Artificial Intelligence enabled solutions both help and harm value creation
• Humans remain morally and economically accountable than machine workforce
• Learning velocity matters more than delivery velocity

The mental model of ALM 1.0 that existed until the late l990 lasted for about 10 years where the mantra was to reduce the life cycle variance. Subsequently, software was considered as a controllable engineering artifact leading to the adoption of the staged software development life cycle (SDLC) approach (Royce, 1970) that relied on documentation with responsibilities delineated across business analysts, system analysts, engineers, quality professionals, and operations. The major challenge was the delayed decision-making and the traceability across these artifacts sometimes maintained across multiple disparate tools which were attributed to incorrect practices (Rajagopalan, 2014) more than the tools. 

The formation of Agile Manifesto in early 2000 (Cunningham, 2001) shaped the mindset for another 10+ years in adopting the adaptive approaches to software development. Subsequently the challenges of focusing on the single source of truth as non-negotiable led to the ALM 2.0 era where the mantra evolved to optimize flow across the cross-functional teams and customer proxy for faster feedback. This made software as a living product that evolved through feedback (Meadows, 2008; Checkland, 1999). Speed was more important than accuracy as continuous delivery in iterative cycles was expected to deliver the quality outcome! The SDLC stages gave in to stages like inception, elaboration, construction, and transition with overlap of responsibilities across the roles. So, the discovery of needs such as the backlog refinement was no longer one person's job and the solution engineering (design, development, delivery, and deployment) was a cross-functional team commitment traceable through integrated artifacts (Rajagopalan, 2019).

The ALM 2.0 was a significant improvement focused on flow and reducing waste! The earlier understanding of the customer needs with faster feedback loops paved the foundation for better solution. But, several anti-patterns continued (Rajagopalan, 2020). Consequently, the tools were abused and misused by siloed teams, role overlading and related ambiguity, lack of customer proximity. These challenges manifested as newer problems like the tool sprawl, ritualized ceremonies, velocity weaponization, and untraceable decisions. 

Despite agility's promises, practice reinvented the same problems just like how waterfall only existed originally in practice and never in theory (Rajagopalan, 2014). Some even thought fancy reports and KPI dashboards would point to the problem failing to recognize that this tool was not the villain and technology was not the problem. The issue was that the premature adoption of technology within the business environment without change management, talent re/upskilling with training, required documentation, provisioning of the access to the tools, and finally the time to execute (prevention costs) were the problem! People failed the processes that created better products!

As part of my consultation of ALM training, project and program training, and coaching and mentoring, I felt that people's resistance to learn and business' need to accelerate failed to reflect on answering powerful questions such as why was some feature prioritized over another, what risks were consciously treated, how optimized were we in monitoring the existing and emerging risks, what alternatives were rejected, and what assumptions could invalidate our continual decisions in value delivery. 

Before this fire could be addressed, the combination of market forces with 4IR technologies with products having cyber-physical interfaces, tool explosion with automated testing, and the emergence of AI with its generative AI capabilities created the perfect storm adding to the existing fire! Discussions in many conferences were not purely technical but how tools supported the decision quality, ethical quality, societal impact, trust quotient, learning debt, automation bias, and model risk! This was about the year 2021 as we were somewhat coming out of the fire of pandemic dragon! This was the time when I felt that the new ALM 3.0 era is emerging.

ALM 3.0 had a new mantra significantly different and also refreshing! This mantra was manage the cost associated with the risks of bad decisions amplified by AI.  I view this ALM 3.0 as a modern way of optimizing cost of quality for the human-AI ecosystem in 4IR and 5IR application lifecycle management space (Domin et al, 2024). It is not just a tool for artifacts to track and trace work. To me, ALM 3.0 reinvigorated the trust preservation framework (Canca, 2020; Kemmel & Vakkuri, 2024) that the ALM tools can promote to track the decision-memory and address the known, unknown, and unknowable risks across all the human (individuals, teams, stakeholders, stakeholder groups) and non-human resources (facilities, equipment, materials, infrastructure, and supplies). Here is my proposed ALM 3.0 architecture across the value delivery framework.

(c) Dr. Sriram Rajagopalan, ALM 3.0 Decision-Making Value Delivery Framework

As I presented these ideas in the Global Conference on Leadership and Project Management revalidating my thoughts, I began asking myself: how can this ALM 3.0 then scale and sustain itself? I felt that convinced that quality was a non-negotiable necessity across the industries globally underpinning the inexorable need to infuse the current cost of quality principles with the modern elements around quality engineering, quality audits, internal quality monitoring and external quality impact. And thus emerged my modern cost of quality foundation!

(c) Dr. Sriram Rajagopalan, Modern Cost of Quality Framework

• The proactive preventions costs no longer rests on training, documentation, equipment, and time to do things right! It also integrates the AI's strength in requirements management, test case authoring, task writing, code writing, risk identification, and risk response planning, for instance. At the same time, treating machine resources (robots, machines, agents) as expendable resources could compromise efficiency! Yes, it is easy to start a new machine on the cloud but can we build a new robot that cleans the floors on the airports? The goal is to continuously learn and guides ourselves on the processes and decisions made. I call this component "Quality Engineering".
• Appraisal costs are not limited monotonous testing and inspections! It expands on them to AI driven anomaly detection, automated and RPA testing, exploratory unscripted testing, and engaging digital twins for performance monitoring. I call this component "Quality Audit".
• Internal failure costs are not rework and scrap work alone! We are now responsible for the costs of decisions delayed or deferred leading to quality slips in the customer's hands. This meant the ALM 3.0 required teams to identify, assess, and treat the non-functional requirements as part of application delivery to monitor triggers that may be hiding problems. 
• Capturing these alerts (e.g.: Health check monitors) and doing AI assisted root cause analysis along with identifying and serving the risk response plans are critical. 
• In manufacturing context, looking at materials proactively and ordering them to limit the WIP, forecast capacity and suggest design recommendations (increased load on machines requiring spinning up machines). 
• With robots, self-autonomous cars, and newly created applications, we may have to evaluate the options for them to see how much calibrate themselves to reconfigure or heal themselves. 
• All of these thoughts may require thinking of alerts (features that people don't see but are required for sustaining these products) and escalate them intelligently up/down the enterprise risk registers. This requires not only technology support but also retrain people on the business processes to be used in the adopted tools. I call this component "Internal Quality Monitoring".
• External failure costs were no longer the challenges of the company dealing with lost business, liability, and warranty! In fact, they took on more support from automation and AI in the forms of:
• Collecting telemetry data to guide what people wanted and where efforts spent didn't generate value (customer retention, hidden things customers can't find, etc.)
• Filtering the signals in the market towards decision-making for products, project, programs, and portfolio. 
• Continuously evaluating our AI models to justify options for customers who can't use the way the systems are designed (the ethical component of justice) and validate themselves (explainability, responsible AI compliance, etc.) I call this component "External Quality Impact".
• Some of these thoughts, I realize, are forward looking! We are not there yet! But, I am sure regulations are catching up, market is moving faster, and customers are demanding more! The questions are: If AI is helping one work faster, why can't value be delivered to us faster too?

What do you think? Please share your opinions! I would love to hear from you as I fill my gaps further.

References

Anil, A.M (2025, Jan 25). Technology convergence is leading the way for the fifth industrial revolution. World Economic Forum. Retrieved from https://www.weforum.org/stories/2025/01/technology-convergence-is-leading-the-way-for-accelerated-innovation-in-emerging-technology-areas/

Boehm, B. W. (1981). Software engineering economics. Prentice Hall.

Bolman, L. G., and Deal, T. E. (2017). Reframing Organizations: Artistry, Choice, and Leadership (6th Ed.) CA: Wiley & Sons, Inc. 

Trevino, L., and Nelson, K. (2017). Managing Business Ethics: Straight Talk About How to Do It Right (7th Ed).  Hoboken, New Jersey: John Wiley & Sons.

Canca, C.: Operationalizing AI ethics principles. Commun. ACM 63(12), 18–21 (2020) Kemell, Kai-Kristian & Vakkuri, Ville. (2024). What Is the Cost of AI Ethics? Initial Conceptual Framework and Empirical Insights. 247-262.

Checkland, P. (1999). Systems thinking, systems practice. John Wiley & Sons.

Cunningham, W. (2001). Manifesto for Agile Software Development. Retrieved from  https://agilemanifesto.org/

Domin, H., Rossi, F. Goehring, B., Ganapini, M., Berente, N., & Bevilacqua, M. (2024, July 29). On the ROI of AI Ethics and Governance Investments: From loss aversion to value generation. California Review Management. https://cmr.berkeley.edu/2024/07/on-the-roi-of-ai-ethics-and-governance-investments-from-loss-aversion-to-value-generation/

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707.

Highsmith, J. (2009). Agile project management: Creating innovative products (2nd ed.). Addison-Wesley.

Meadows, D. H. (2008). Thinking in systems: A primer. Chelsea Green Publishing.

 

Rajagopalan, S. (2025). Leadership Unleashed: Game Changing Insights. Denver, CO: Outskirts Press.

Rajagopalan, S. (2020, Jan 11). What Patterns to avoid in Agile Ceremonies? Retrieved from  https://youtu.be/i7nR3gn34Go

Rajagopalan, S. (2019, Jan 30). Five Principles for Managing Your Application Lifecycle with SpiraTeam. Retrieved from https://youtu.be/s2Z41C4W1mU

Rajagopalan, S. (2018, June). Leadership Simplified: Leaders Must SLEEP. IEEE Engineering Management Review, 46 (2), 18-20. 

Rajagopalan, S. (2016, July). TONES: A reference framework for identifying skills and competencies and grooming talent to transform middle management through the field of Project Management. International Journal of Markets and Business Systems, 2(1), 3-24. 

Rajagopalan, S. (2015). Product personification: PARAG model to successful software product development. International Journal of Managing Value and Supply Chains, 6(1), 1-12. 

Rajagopalan, S. (2014). Review of the myths on original software development model. International Journal of Software Engineering & Applications, 5(16), 103-111.

Rothaermel, F. T. (2019). Strategic management (4th ed.). New York, NY: McGraw-Hill Education. 

Royce, W. W. (1970) “Managing the development of large software systems”, Proceedings, IEEE WESCON, August, pp 1-9.

The 5 I’s of Stakeholder Engagement: Building Stronger Connections

I often say, "Tell me about the stakeholders identified and your engagement strategy for your project, I will tell you how successful your project will be!" My thoughts were modelled after the saying, "Tell me your friends and I will tell you who you are!" Since stakeholders are people that can positively or negatively influence or be impacted by the project's outcome, it is important to understand the insurmountable role of stakeholders in delivering the 5P's (project, process, product, program, and portfolio) of value delivery! 

Furthermore, since stakholders are not members that we can directly manage, we need to see how we can engage them effectively. This is crucial not only for the 5P's success but also for strategic growth of the organization and fostering strong partnerships for verticial and horizontal growth. In this regard, I feel that there are five magic ingredients of stakeholder engagemen. These are interest, involvement, interdependencies, influence, and impact. These elements help leaders navigate complex relationships and align objectives facilitating execution as well as governance. Understanding these dimensions ensures that stakeholders remain engaged, informed, and motivated throughout the journey.

1. Interest (Care or Concern)
Stakeholders must have a clear interest in the project or initiative. Identifying what motivates them—be it financial returns, innovation, or social responsibility—helps shape engagement strategies. Here, I come up with both care and concern. Care gives a positive spin of stakeholders while concern may give the adverse considerations they may have. In both cases, if they are silent observers, creative ideas to problem solving and decision making are are left out. So, the extent of the involvement is the next thing to understand. 

2. Involvement
Once interest is established, involvement becomes key. Encouraging active participation through workshops, feedback loops, and collaborative decision-making strengthens their commitment and enhances project outcomes. While adaptive approaches talk about "buiness people and developers must engage on a daily basis" to emphasise their involvement, plan-driven approaches also promote similar thoughts of specific stage/phase gate reviews. In both these project delivery approaches, it is important to understand how much stakeholders are involved proactively! The sooner you understand this engagement, the earlier you address risks through preventive action. 

3. Interdependencies
Stakeholders do not exist in isolation. Business units have their own objectives as part of their goals to serve the organizational objectives. So, in all the elements of 5P's, the stakeholders interact with each other in various ways, impacting (which by the way is the fifth "I") project dynamics. Recognizing interdependencies allows for strategic alignment, reducing friction and maximizing collaboration. Preparing the people in advance of how our work impacts others builds the surround sound required for success.

4. Influence
The combination of interest, involvement, and interdependcies are not adequate if one does not support the overarching objetctives by championing change. Influence therefore connects with the behavioral change people can exercise not only by hiearchical authority but also by expertise they bring to the team. Influence determines how much power stakeholders wield over decisions. Understanding their authority, expertise, and networks helps in prioritizing engagement efforts effectively.

5. Impact
At the heart of stakeholder engagement is impact—how actions and decisions affect both the project and the stakeholders themselves. Effective engagement strategies create value for all parties, fostering trust and long-term relationships. By aligning interests, managing influence, and leveraging interdependencies, leaders can drive meaningful change, ensuring projects achieve sustainable success.

So, operating between the two bookends (interest and impact) lies the involvement, interdependencies, and influence. While techniques such as the stakeholder register, stakeholder engaement assessment matrix, power-interest grid, salience model, and stakeholder map exist, the stakeholder engagement is more of an art than science! It comes only with practice!

Don't you think so? Thoughts?

Demonstration of Leadership in Instructional Design

I spoke in a conference focusing on the role of emerging faculty in leading learners in the AI driven world at Northeastern University College of Professional Studies Faculty Conference in Oct 2024. While there were a lot of concerns on the adoption of AI due to the plagiarism, the major concerns was towards the lack of critical thinking as people can always get the answers! The goal has become more on prompt engineering rather than critical thinking! 

While prompt engineering itself is not bad as it also makes one think of the Five Whys or Socratic thinking approaches, I found one example of a middle school learner asking why they should know about using calculators when they can ask Siri or Alexa! Perhaps I am outdate in my thoughts that I used the logarithmic book once upon a time. While scientific calculator was great, when it broke, I was not stuck! But, now, I see people not knowing how to read time on an analog clock or not knowing how to find time when their Alexa is out of commission! 

As I thought through ideas, I came up with a handout that trainers and teachers can use to augment critical thinking with specific methods and some tools. People felt and have since then mentioned that this was a very good resource and so I am sharing for my readers.

PROMOTE AI LITERACY AND CRITICAL THINKING 

Methods 

• Integrate AI into the curriculum, discussing its capabilities and limitations. 

• Teach students to use AI responsibly, i.e. fact-check and verify AI outputs 

• Encourage and invite students to critically evaluate AI-generated content 

• Assign projects that involve using and analyzing AI tools. 

Tools 

• AI ethics courses (e.g., MIT's "Ethics of AI" course materials). 

• Critical thinking frameworks (e.g., CRAAP test for evaluating sources). 

• AI-powered fact-checking tools (e.g., Factmata, Full Fact.) 

 

USE AI FOR PERSONALIZED LEARNING EXPERIENCES 

Methods 

• Use AI to ensure that lesson plans are current and relevant. 

• Use AI-powered tutoring systems for additional support. 

• Customize individual study plans based on AI analysis of student performance. 

• Employ AI to identify learning gaps and suggest targeted resources. 

Tools 

• Experiment with custom agents like Claude, OpenAI, Copilot, and Gemini. 

• Research AI thinking courses on MOOC and include as supporting content. 

• Evaluate dashboards of student learning outcomes weekly.  

 

LEVERAGE AI FOR AUTOMATED GRADING AND FEEDBACK 

Methods 

• Use AI to grade objective assessments and provide instant feedback. 

• Implement AI-powered writing evaluation tools for essays and reports. 

• Use AI to evaluate student work for plagiarism, ensure compliance with academic integrity standards. 

• Combine AI grading with human review for a balanced approach. 

Tools 

• Automated essay scoring systems (e.g., ETS e-rater). 

• Plagiarism detection software (e.g., Duplichecker, Turnitin, Copyleaks). 

• AI-powered feedback tools (e.g., Revision Assistant, WriteLab). 

 

INCORPORATE AI-ASSISTED CONTENT CREATION 

Methods 

• Use AI to generate diverse examples and practice problems. 

• Employ AI tools to create multimedia content for lessons. 

• Utilize AI for language translation to support multilingual classrooms. 

• Collaborate with AI to develop interactive simulations and scenarios. 

Tools 

• AI content generators (e.g., GPT-3 based tools can help with PPP structures.) 

• Text-to-speech and speech-to-text tools (e.g., Amazon Polly, Google Cloud Speech-to-Text.) 

• AI-powered design tools (e.g., Canva AI, Adobe Sensei); virtual reality platforms (eg. Labster, zSpace).