Tag Archives: could


One example of a capstone project in computer science would be developing a customized medical information system for a clinic or hospital. For a project of this scope and scale, students would work in a team to analyze requirements, design the system architecture, develop the necessary code and applications, implement security features, test all aspects of the system, and deploy it for real-world use at the medical facility.

In the initial phases, the student team would work closely with administrators, doctors, nurses and other medical staff at the facility to understand their detailed workflow processes, data storage and reporting needs, and systems integration requirements. This requirements gathering and analysis phase is crucial to understand all of the features and functionality that must be included in the custom medical information system. The team would document gathered requirements, perform gap analysis on current workflows versus desired future state, and prioritize features to ensure the system addresses top priorities and pain points.

With a comprehensive understanding of requirements in hand, the student team would then begin designing the system architecture. Key consideration would include decisions around database structure and schemas, backend application design using appropriate programming languages and frameworks, front-end user interface designs for various user roles (doctors, nurses, administrators etc.), integration with existing practice management systems or electronic health records if needed. Important non-functional requirements around security, privacy, performance, scalability and maintainability would also influence architectural design decisions.

Detailed documentation of the system architecture design would be created, covering database models, application component diagrams, interface wireframes, infrastructure requirements and more. Students would present and defend their proposed architecture to stakeholders to obtain feedback and approval before moving to implementation.

The implementation phase represents the bulk of effort for the project where students translate designs into working code and applications. Key activities would include:

Building out the backend applications using languages like PHP, Python, Java or .NET to implement the required functionality based on requirements and architectural designs. This includes developing APIs, business logic and integration layers.

Creating a frontend UI using HTML, CSS and JavaScript frameworks like React or Angular that adheres to user experience designs and provides role-based interfaces.

Setting up and configuring a database like MySQL, SQL Server or MongoDB based on the data models and architecting appropriate schemas, indexes, foreign keys etc.

Populating the database with sample test data including demo patient records, appointment schedules, insurance profiles and more to enable thorough testing later.

Integrating the custom system with other existing medical facility systems like practice management software or EHR products through pre-defined APIs.

Implementing security features like multi-factor authentication, authorization controls, encrypted data transfer and storage, input validation etc. based on a thorough security risk assessment.

Developing comprehensive installation, configuration and operation guides for medical staff.

Performing extensive testing of all functionality from different user perspectives to uncover bugs. This includes unit testing code, integration testing, user acceptance testing, load/stress testing and more.

Once development is complete, the student team would help deploy and launch the new medical information system at the partner medical facility. This includes performing the necessary installation and configuration activities, onboarding and training of medical staff, addressing any post-deployment issues, and measuring success based on defined key performance indicators.

Ongoing maintenance and improvements to the system over several months post deployment may also be part of the project scope, requiring the team to monitor system performance, implement requested enhancements, and resolve production issues.

In the concluding project phases, the student team would document the complete system development lifecycle and create a comprehensive final report. An oral presentation would be given to stakeholders highlighting achievements, lessons learned, future roadmap for the system and reflections on career readiness gained through such a hands-on capstone project experience.

An example medical information system capstone project as outlined above covers the full scope from requirements analysis to deployment, addresses real-world problems through technical solutions, and provides students an in-depth industry-aligned experience to showcase their cumulative skills and knowledge gained throughout their computer science education. Completing a complex project of this scale truly allows students to synthesize their learning and strengthens their career preparedness for jobs in both software development and healthcare IT fields.


Rare cancers that affect children are of particular interest for capstone projects because they often receive less research funding and attention compared to more common adult cancers. Developing a deeper understanding of the molecular mechanisms, treatments, and patients’ experiences with rare pediatric cancers can help advance care for these vulnerable populations. Here are some examples of rare pediatric cancers that would be suitable topics for an in-depth senior or graduate-level capstone project:

Neuroblastoma is a rare cancer that forms in certain types of nerve tissue and most commonly appears in young children, often presenting in the adrenal glands, chest, abdomen or neck. It accounts for around 15% of all childhood cancers but less than 1% of all cancers diagnosed. Despite being rare, neuroblastoma is responsible for more deaths among children with solid tumors than any other cancer. A capstone project could explore new targeted therapies and immunotherapies in development for high-risk neuroblastoma. The student could conduct a literature review of recent clinical trials and analyze molecular markers to identify patient subgroups most likely to respond to certain treatments. Understanding the genetics and biology of neuroblastoma in more detail could help accelerate the development of personalized, precision medicine approaches.

Ewing sarcoma is the second most common bone cancer in children after osteosarcoma. It remains quite rare, accounting for less than 1% of all cancers and 3% of childhood cancers. Ewing sarcoma most often appears in bones of the pelvis, legs, chest, or spine and is characterized by translocations linking the EWS gene to an ETS family gene. A capstone project on Ewing sarcoma could comprehensively review past and current standard of care therapies, while also evaluating promising new targeted drugs and immunotherapies in preclinical and early phase clinical testing. Interviews with patients, families and clinicians could provide insights into the challenges of living with and treating this aggressive bone cancer. Identifying biomarkers for early detection and response to treatment is another important area warranting further research highlighted by such a project.

Rhabdomyosarcoma is a type of soft tissue sarcoma that develops from skeletal muscle cells or muscles in other parts of the body. It represents about 3-4% of all childhood cancers but is still considered rare. The most common locations are the head and neck region, genitourinary tract, and extremities. Subtypes include embryonal, alveolar and pleomorphic. A capstone project could focus specifically on the more aggressive alveolar subtype, analyzing its distinctive genetic mutations and exploring combination therapies to overcome resistance. The student might profile a series of alveolar rhabdomyosarcoma cases at their institution to identify clinical or molecular characteristics associated with improved outcomes. Interviews with long-term survivors could offer unique perspectives on the emotional and physical impacts as well as care needs over time.

Atypical teratoid/rhabdoid tumor (AT/RT) is an extremely rare and highly malignant type of cancerous brain tumor that primarily affects young children. It develops from cells in the central nervous system and has a very poor prognosis despite intensive multimodal therapy. AT/RT represents less than 1% of all pediatric central nervous system tumors but is the focus of considerable research efforts given its lethal nature. A project delving into the molecular hallmarks and epigenetic dysregulation characteristic of AT/RT could survey targeted agents in preclinical testing and early stage clinical trials. Collaboration with neuro-oncologists may provide access to tumor samples for exploring biomarkers of sensitivity and resistance. Investigating supportive care interventions and quality of life for patients undergoing complex treatment regimens could also yield important insights.

Wilms tumor, also known as nephroblastoma, begins in the kidneys and is the most common malignant tumor of the kidneys in children. It represents approximately 6% of all childhood cancers yet remains defined as a rare cancer. Wilms tumor is usually found in children younger than 5 years old, with 80-90% of cases arising before the age of 6. A capstone topic could extensively review protocols from cooperative clinical trials groups to analyze factors influencing event-free survival overtime. The student might conduct interviews with nursing professionals and child life specialists to gain perspective on psychosocial support needs throughout the patient journey. Exploration of genomic characterization efforts aimed at more precisely stratifying risk could also yield valuable insights for precision oncology approaches.

Rare pediatric cancers like neuroblastoma, Ewing sarcoma, rhabdomyosarcoma, AT/RT and Wilms tumor present opportunities for in-depth capstone study. Delving into disease biology, therapeutic developments, clinical research challenges, and patient/family experiences could advance understanding and care for these underserved populations. With a comprehensive literature review augmented by primary data collection, a student could produce an original research project meaningfully contributing to progress against devastating pediatric cancers.


Narrow artificial intelligence (AI) refers to AI systems that are designed and trained to perform a specific task, such as playing chess, driving a car, answering customer service queries or detecting spam emails. In contrast, general artificial intelligence (AGI) describes a hypothetical AI system that demonstrates human-level intelligence and mental flexibility across a broad range of cognitive tasks and environments. Such a system does not currently exist.

Narrow AI is also known as weak AI, specific AI or single-task AI. These systems are focused on narrowly defined tasks and they are not designed to be flexible or adaptable. They are programmed to perform predetermined functions and do not have a general understanding of the world or the capability to transfer their knowledge to new problem domains. Examples of narrow AI include algorithms developed for image recognition, machine translation, self-driving vehicles and conversational assistants like Siri or Alexa. These systems excel at their specialized functions but lack the broader general reasoning abilities of humans.

Narrow AI systems are created using techniques of artificial intelligence like machine learning, deep learning or computer vision. They are given vast amounts of example inputs to learn from, known as training data, which helps them perform their designated tasks with increasing accuracy. Their capabilities are limited to what they have been explicitly programmed or trained for. They do not have a general, robust understanding of language, common sense reasoning or contextual pragmatics like humans do. If the input or environment changes in unexpected ways, their performance can deteriorate rapidly since they lack flexibility.

Some key characteristics of narrow AI systems include:

They are focused on a narrow, well-defined task like classification, prediction or optimization.

Their intelligence is limited to the specific problem domain they were created for.

They lack general problem-solving skills and an understanding of abstract concepts.

Reprising the same task in a new context or domain beyond their training scope is challenging.

They have little to no capability of self-modification or learning new skills independently without reprogramming.

Their behavior is limited to what their creators explicitly specified during development.

General artificial intelligence, on the other hand, aims to develop systems that can perform any intellectual task that a human can. A true AGI would have a wide range of mental abilities such as natural language processing, common sense reasoning, strategic planning, situational adaptation and the capability to autonomously acquire new skills through self-learning. Some key hypothetical properties of such a system include:

It would have human-level intelligence across diverse domains rather than being narrow in scope.

Its core algorithms and training methodology would allow continuous open-ended learning from both structured and unstructured data, much like human learning.

It would demonstrate understanding, not just performance, and be capable of knowledge representation, inference and abstract thought.

It could transfer or generalize its skills and problem-solving approaches to entirely new situations, analogous to human creativity and flexibility.

Self-awareness and consciousness may emerge from sufficiently advanced general reasoning capabilities.

Capable of human-level communication through natural language dialogue rather than predefined responses.

Able to plan extended sequences of goals and accomplish complex real-world tasks without being explicitly programmed.

Despite several decades of research, scientists have not achieved anything close to general human-level intelligence so far. The sheer complexity and open-ended nature of human cognition present immense scientific challenges to artificial general intelligence. Most experts believe true strong AGI is still many years away, if achievable at all given our current understanding of intelligence. Research into more general and scalable machine learning algorithms is bringing us incrementally closer.

While narrow AI is already widely commercialized, AGI would require enormous computational resources and exponentially more advanced machine learning techniques that are still in early research stages. Narrow AI systems are limited but very useful for improving specific application domains like entertainment, customer service, transportation etc. General intelligence remains a distant goal though catalysts like advanced neural networks, increasingly large datasets and continued Moore’s Law scaling of computing power provide hope that it may eventually become possible to develop an artificial general intelligence as powerful as the human mind. There are also open questions about the control and safety of super-intelligent machines which present research challenges of their own.

Narrow AI and general AI represent two points on a spectrum of machine intelligence. While narrow AI already delivers substantial economic and quality of life benefits through focused applications, general artificial intelligence aiming to match human mental versatility continues to be an ambitious long term research goal.Future generations of increasingly general and scalable machine learning may potentially bring us closer to strong AGI, but its feasibility and timeline remain uncertain given our incomplete understanding of intelligence itself.


Documentation is essential for ensuring the capstone project work is well recorded and can be understood by others. It provides a record of the process that was undertaken to complete the project from concept to execution. Thorough documentation demonstrates the research, planning, methodology, outputs and results of the project work. It allows others to understand the thought process and technical details of how and why certain decisions were made. Documentation serves several important purposes for a capstone project:

It acts as an historical record of the full scope of work so future readers have context on the project background, goals, development and outcomes. This is important for project replication or building upon the work in the future.

Documentation helps demonstrate the complex problem solving and analytical thinking undertaken during the project. It conveys the process of investigating challenges, weighing design options, testing solutions and improving based on results. This showcases the higher-level skills developed through the capstone experience.

Maintaining documentation throughout the project allows for periodic review of progress and course corrections if needed. It supports ongoing planning, monitoring and evaluation of whether project aims are being successfully achieved.

The documentation provides raw materials, notes, data collection instruments and interim or failed results for inclusion in a final capstone report or thesis. This evidences the breadth and depth of effort.

Thorough documentation facilitates supervisor/advisor oversight and guidance. It allows them to understand project progress, provide timely feedback and ensure the work remains on track to meet requirements.

Documentation acts as a reference guide for how to replicate processes, techniques or solutions developed through the project. This reference aspect supports knowledge sharing and application of lessons learned to future initiatives.

Documentation materials may be included as appendices or supplemental files in the final capstone submission. This enrichment enhances understanding of the full scope and process behind the reported results.

Documentation sets the stage for potential publication, presentation or further development of project insights and outcomes. It preserves intellectual property and attributions should any aspects warrant continued research, commercialization or application post-capstone.

Presentation of the capstone work is also critical for effectively communicating the project experience and outcomes to others. Presentation allows the student to tell the full story of their capstone journey in a compelling format and have their work evaluated based on how clearly and convincingly they are able to convey it. The presentation provides an opportunity to:

Synthesize and highlight the most important aspects of documentation in a summative manner using visual and oral presentation tools. This distills down copious notes and materials into a clear narrative.

Demonstrate public speaking, presentation development and delivery skills learned through completion of the extensive capstone project. Concisely sharing findings lends itself well to showcasing communication talents.

Stimulate interest and engage audience members by painting a picture of the motivation, aims and significance of the work in a memorable format. Storytelling abilities are emphasized.

Provide a question and answer period where deeper understanding, remaining questions and next steps can be explored interactively. This facilitates two-way knowledge exchange.

Receive valuable feedback on the merits and limitations of approaches, outcomes, analyses as well as on the presentation style itself. Suggestions for improvement are garnered.

Express passion, confidence and mastery over the topic after investing major effort into planning and implementing the capstone study. Presentation validates competence.

Formally report conclusions, implications, lessons learned and impact made through completion of the project. Persuasiveness of arguments is tested.

Allow work to be critiqued by the broader community of peers, faculty and industry partners. Increased exposure for potential applications results.

Thorough documentation accompanied by an effective presentation is vital for demonstrating full achievement and sharing the fruits of capstone projects. Together, they support evaluating comprehensive understanding, application of knowledge and communication skills developed through this culminating undergraduate experience. Proper attention to documentation and presentation ensures maximum learning and future impact from the capstone work.


The widespread adoption of self-driving vehicles has the potential to significantly impact many existing jobs. One of the largest and most obvious job categories that could see major losses is commercial drivers such as taxi drivers, ride-hailing drivers such as Uber and Lyft operators, truck drivers, and bus drivers. According to estimates from the U.S. Bureau of Labor Statistics, there are over 3.5 million Americans employed as drivers of taxi cabs and ride-hailing vehicles, heavy and tractor-trailer truck drivers, and bus drivers. With self-driving vehicles able to operate without a human driver, the need for people to operate vehicles for a living would greatly diminish.

While self-driving trucks may still require drivers as attendants initially, the role would be more supervisory than operational driving the vehicle. Over time, the job functions of commercial drivers could be eliminated altogether as technology advances. This would result in massive job losses across these commercial driving industries that currently employ millions. Commercial driving also has many ancillary jobs associated with it such as truck stop employees, repair shop workers, weight station attendants, and others that could see reduced demand. The impact would ripple through local economies that rely heavily on commercial transportation.

In addition to commercial drivers, many automotive industry jobs could be affected. Mechanics focused on repairing and maintaining human-operated vehicles may see reduced demand for their services. As self-driving vehicles rely more on software, communication systems, and sensor technologies rather than mechanical components, the needs of vehicles will change. While new technical mechanic and repair jobs may emerge to service autonomous technologies, many existing mechanic specializations could become obsolete. Manufacturing line workers building vehicles may also face risks. As vehicles require fewer human-centric components and more computers and automation, production facilities would likely require fewer workers and adopt more industrial robotics.

Complementing the mechanical and manufacturing implications are a variety of jobs in supporting industries. From vendors that serve gas stations and truck stops to motels along highways that rely on commercial driver customers, many local businesses could take an economic hit from less vehicle traffic operated by humans. Roadside assistance workers like tow truck drivers may have lower call volumes as self-driving vehicles have fewer accidents and need less aid with tasks like jump starts. Even industries like motor vehicle parts suppliers, car washes, and parking facilities could see their customer base erode over time with autonomous vehicles that require less human oversight and operation.

Insurance and finance sector jobs linked to vehicle ownership may also see reallocation. Roles associated with insuring human drivers against issues like accidents and liabilities would logically decline if robot-driven cars cause drastically fewer crashes. Auto insurance models and underwriting specialists may need to shift focus. On the lending side, banks and finance companies that currently provide loans and financing packages for vehicle purchases may originate fewer new loans as shared mobility further reduces private car ownership. Related customer service and debt collection roles could consequently contract. Real estate could additionally feel impacts, as autonomous vehicles may reduce demand for non-residential developments centered around human transportation needs from gas stations to parking decks.

While the nature of many transportation planning, urban design, traffic engineering and government regulatory jobs would transition alongside autonomous vehicle integration, overall staffing levels in these fields may not necessarily decrease. Without intervention, job losses across whole sectors like commercial driving could number in the millions. Proactive workforce retraining programs and policy will be crucial to help displaced workers transition skills and find new occupations. There would surely be many new types of jobs created to develop, deploy and maintain autonomous vehicle systems, but the costs of lost jobs may unfortunately outweigh the benefits for some time without strategies to support workers through change. Widespread autonomous vehicle adoption holds potential economic gains, but also significant risks to employment that responsible leaders must address proactively to manage impacts. The changes will be massive, and managing this transition effectively will be one of the great challenges in developing self-driving technology for the benefit of society.