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Research Paper Rubric

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Teaching excellence & educational innovation, grading and performance rubrics, what are rubrics.

A rubric is a scoring tool that explicitly represents the performance expectations for an assignment or piece of work. A rubric divides the assigned work into component parts and provides clear descriptions of the characteristics of the work associated with each component, at varying levels of mastery. Rubrics can be used for a wide array of assignments: papers, projects, oral presentations, artistic performances, group projects, etc. Rubrics can be used as scoring or grading guides, to provide formative feedback to support and guide ongoing learning efforts, or both.

Advantages of Using Rubrics

Using a rubric provides several advantages to both instructors and students. Grading according to an explicit and descriptive set of criteria that is designed to reflect the weighted importance of the objectives of the assignment helps ensure that the instructor’s grading standards don’t change over time. Grading consistency is difficult to maintain over time because of fatigue, shifting standards based on prior experience, or intrusion of other criteria. Furthermore, rubrics can reduce the time spent grading by reducing uncertainty and by allowing instructors to refer to the rubric description associated with a score rather than having to write long comments. Finally, grading rubrics are invaluable in large courses that have multiple graders (other instructors, teaching assistants, etc.) because they can help ensure consistency across graders and reduce the systematic bias that can be introduced between graders.

Used more formatively, rubrics can help instructors get a clearer picture of the strengths and weaknesses of their class. By recording the component scores and tallying up the number of students scoring below an acceptable level on each component, instructors can identify those skills or concepts that need more instructional time and student effort.

Grading rubrics are also valuable to students. A rubric can help instructors communicate to students the specific requirements and acceptable performance standards of an assignment. When rubrics are given to students with the assignment description, they can help students monitor and assess their progress as they work toward clearly indicated goals. When assignments are scored and returned with the rubric, students can more easily recognize the strengths and weaknesses of their work and direct their efforts accordingly.

Examples of Rubrics

Here are links to a diverse set of rubrics designed by Carnegie Mellon faculty and faculty at other institutions. Although your particular field of study and type of assessment activity may not be represented currently, viewing a rubric that is designed for a similar activity may provide you with ideas on how to divide your task into components and how to describe the varying levels of mastery.

Paper Assignments

• Example 1: Philosophy Paper This rubric was designed for student papers in a range of philosophy courses, CMU.
• Example 2: Psychology Assignment Short, concept application homework assignment in cognitive psychology, CMU.
• Example 3: Anthropology Writing Assignments This rubric was designed for a series of short writing assignments in anthropology, CMU.
• Example 4: History Research Paper . This rubric was designed for essays and research papers in history, CMU.
• Example 1: Capstone Project in Design This rubric describes the components and standard of performance from the research phase to the final presentation for a senior capstone project in the School of Design, CMU.
• Example 2: Engineering Design Project This rubric describes performance standards on three aspects of a team project: Research and Design, Communication, and Team Work.

Oral Presentations

• Example 1: Oral Exam This rubric describes a set of components and standards for assessing performance on an oral exam in an upper-division history course, CMU.
• Example 2: Oral Communication
• Example 3: Group Presentations This rubric describes a set of components and standards for assessing group presentations in a history course, CMU.

Class Participation/Contributions

• Example 1: Discussion Class This rubric assesses the quality of student contributions to class discussions. This is appropriate for an undergraduate-level course, CMU.
• Example 2: Advanced Seminar This rubric is designed for assessing discussion performance in an advanced undergraduate or graduate seminar. 

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Whenever we give feedback, it inevitably reflects our priorities and expectations about the assignment. In other words, we're using a rubric to choose which elements (e.g., right/wrong answer, work shown, thesis analysis, style, etc.) receive more or less feedback and what counts as a "good thesis" or a "less good thesis." When we evaluate student work, that is, we always have a rubric. The question is how consciously we’re applying it, whether we’re transparent with students about what it is, whether it’s aligned with what students are learning in our course, and whether we’re applying it consistently. The more we’re doing all of the following, the more consistent and equitable our feedback and grading will be:

Being conscious of your rubric ideally means having one written out, with explicit criteria and concrete features that describe more/less successful versions of each criterion. If you don't have a rubric written out, you can use this assignment prompt decoder for TFs & TAs to determine which elements and criteria should be the focus of your rubric.

Being transparent with students about your rubric means sharing it with them ahead of time and making sure they understand it. This assignment prompt decoder for students is designed to facilitate this discussion between students and instructors.

Aligning your rubric with your course means articulating the relationship between “this” assignment and the ones that scaffold up and build from it, which ideally involves giving students the chance to practice different elements of the assignment and get formative feedback before they’re asked to submit material that will be graded. For more ideas and advice on how this looks, see the " Formative Assignments " page at Gen Ed Writes.

Applying your rubric consistently means using a stable vocabulary when making your comments and keeping your feedback focused on the criteria in your rubric.

How to Build a Rubric

Rubrics and assignment prompts are two sides of a coin. If you’ve already created a prompt, you should have all of the information you need to make a rubric. Of course, it doesn’t always work out that way, and that itself turns out to be an advantage of making rubrics: it’s a great way to test whether your prompt is in fact communicating to students everything they need to know about the assignment they’ll be doing.

So what do students need to know? In general, assignment prompts boil down to a small number of common elements :

• Evidence and Analysis
• Style and Conventions
• Specific Guidelines
• Advice on Process

If an assignment prompt is clearly addressing each of these elements, then students know what they’re doing, why they’re doing it, and when/how/for whom they’re doing it. From the standpoint of a rubric, we can see how these elements correspond to the criteria for feedback:

All of these criteria can be weighed and given feedback, and they’re all things that students can be taught and given opportunities to practice. That makes them good criteria for a rubric, and that in turn is why they belong in every assignment prompt.

Which leaves “purpose” and “advice on process.” These elements are, in a sense, the heart and engine of any assignment, but their role in a rubric will differ from assignment to assignment. Here are a couple of ways to think about each.

On the one hand, “purpose” is the rationale for how the other elements are working in an assignment, and so feedback on them adds up to feedback on the skills students are learning vis-a-vis the overall purpose. In that sense, separately grading whether students have achieved an assignment’s “purpose” can be tricky.

On the other hand, metacognitive components such as journals or cover letters or artist statements are a great way for students to tie work on their assignment to the broader (often future-oriented) reasons why they’ve been doing the assignment. Making this kind of component a small part of the overall grade, e.g., 5% and/or part of “specific guidelines,” can allow it to be a nudge toward a meaningful self-reflection for students on what they’ve been learning and how it might build toward other assignments or experiences.

Advice on process

As with “purpose,” “advice on process” often amounts to helping students break down an assignment into the elements they’ll get feedback on. In that sense, feedback on those steps is often more informal or aimed at giving students practice with skills or components that will be parts of the bigger assignment.

For those reasons, though, the kind of feedback we give students on smaller steps has its own (even if ungraded) rubric. For example, if a prompt asks students to  propose a research question as part of the bigger project, they might get feedback on whether it can be answered by evidence, or whether it has a feasible scope, or who the audience for its findings might be. All of those criteria, in turn, could—and ideally would—later be part of the rubric for the graded project itself. Or perhaps students are submitting earlier, smaller components of an assignment for separate grades; or are expected to submit separate components all together at the end as a portfolio, perhaps together with a cover letter or artist statement .

Using Rubrics Effectively

In the same way that rubrics can facilitate the design phase of assignment, they can also facilitate the teaching and feedback phases, including of course grading. Here are a few ways this can work in a course:

Discuss the rubric ahead of time with your teaching team. Getting on the same page about what students will be doing and how different parts of the assignment fit together is, in effect, laying out what needs to happen in class and in section, both in terms of what students need to learn and practice, and how the coming days or weeks should be sequenced.

Share the rubric with your students ahead of time. For the same reason it's ideal for course heads to discuss rubrics with their teaching team, it’s ideal for the teaching team to discuss the rubric with students. Not only does the rubric lay out the different skills students will learn during an assignment and which skills are more or less important for that assignment,  it means that the formative feedback they get along the way is more legible as getting practice on elements of the “bigger assignment.” To be sure, this can’t always happen. Rubrics aren’t always up and running at the beginning of an assignment, and sometimes they emerge more inductively during the feedback and grading process, as instructors take stock of what students have actually submitted. In both cases, later is better than never—there’s no need to make the perfect the enemy of the good. Circulating a rubric at the time you return student work can still be a valuable tool to help students see the relationship between the learning objectives and goals of the assignment and the feedback and grade they’ve received.

Discuss the rubric with your teaching team during the grading process. If your assignment has a rubric, it’s important to make sure that everyone who will be grading is able to use the rubric consistently. Most rubrics aren’t exhaustive—see the note above on rubrics that are “too specific”—and a great way to see how different graders are handling “real-life” scenarios for an assignment is to have the entire team grade a few samples (including examples that seem more representative of an “A” or a “B”) and compare everyone’s approaches. We suggest scheduling a grade-norming session for your teaching staff.

• Designing Your Course
• In the Classroom
• When/Why/How: Some General Principles of Responding to Student Work
• Consistency and Equity in Grading
• Assessing Class Participation
• Assessing Non-Traditional Assignments
• Beyond “the Grade”: Alternative Approaches to Assessment
• Getting Feedback
• Equitable & Inclusive Teaching
• Advising and Mentoring
• Teaching and Your Career
• Teaching Remotely
• Tools and Platforms
• The Science of Learning
• Bok Publications
• Other Resources Around Campus

Rubric Best Practices, Examples, and Templates

A rubric is a scoring tool that identifies the different criteria relevant to an assignment, assessment, or learning outcome and states the possible levels of achievement in a specific, clear, and objective way. Use rubrics to assess project-based student work including essays, group projects, creative endeavors, and oral presentations.

Rubrics can help instructors communicate expectations to students and assess student work fairly, consistently and efficiently. Rubrics can provide students with informative feedback on their strengths and weaknesses so that they can reflect on their performance and work on areas that need improvement.

How to Get Started

Best practices, moodle how-to guides.

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Step 1: Analyze the assignment

The first step in the rubric creation process is to analyze the assignment or assessment for which you are creating a rubric. To do this, consider the following questions:

• What is the purpose of the assignment and your feedback? What do you want students to demonstrate through the completion of this assignment (i.e. what are the learning objectives measured by it)? Is it a summative assessment, or will students use the feedback to create an improved product?
• Does the assignment break down into different or smaller tasks? Are these tasks equally important as the main assignment?
• What would an “excellent” assignment look like? An “acceptable” assignment? One that still needs major work?
• How detailed do you want the feedback you give students to be? Do you want/need to give them a grade?

Step 2: Decide what kind of rubric you will use

Types of rubrics: holistic, analytic/descriptive, single-point

Holistic Rubric. A holistic rubric includes all the criteria (such as clarity, organization, mechanics, etc.) to be considered together and included in a single evaluation. With a holistic rubric, the rater or grader assigns a single score based on an overall judgment of the student’s work, using descriptions of each performance level to assign the score.

Advantages of holistic rubrics:

• Can p lace an emphasis on what learners can demonstrate rather than what they cannot
• Save grader time by minimizing the number of evaluations to be made for each student
• Can be used consistently across raters, provided they have all been trained

Disadvantages of holistic rubrics:

• Provide less specific feedback than analytic/descriptive rubrics
• Can be difficult to choose a score when a student’s work is at varying levels across the criteria
• Any weighting of c riteria cannot be indicated in the rubric

Analytic/Descriptive Rubric . An analytic or descriptive rubric often takes the form of a table with the criteria listed in the left column and with levels of performance listed across the top row. Each cell contains a description of what the specified criterion looks like at a given level of performance. Each of the criteria is scored individually.

Advantages of analytic rubrics:

• Provide detailed feedback on areas of strength or weakness
• Each criterion can be weighted to reflect its relative importance

Disadvantages of analytic rubrics:

• More time-consuming to create and use than a holistic rubric
• May not be used consistently across raters unless the cells are well defined
• May result in giving less personalized feedback

Single-Point Rubric . A single-point rubric is breaks down the components of an assignment into different criteria, but instead of describing different levels of performance, only the “proficient” level is described. Feedback space is provided for instructors to give individualized comments to help students improve and/or show where they excelled beyond the proficiency descriptors.

Advantages of single-point rubrics:

• Easier to create than an analytic/descriptive rubric
• Perhaps more likely that students will read the descriptors
• Areas of concern and excellence are open-ended
• May removes a focus on the grade/points
• May increase student creativity in project-based assignments

Disadvantage of analytic rubrics: Requires more work for instructors writing feedback

Step 3 (Optional): Look for templates and examples.

You might Google, “Rubric for persuasive essay at the college level” and see if there are any publicly available examples to start from. Ask your colleagues if they have used a rubric for a similar assignment. Some examples are also available at the end of this article. These rubrics can be a great starting point for you, but consider steps 3, 4, and 5 below to ensure that the rubric matches your assignment description, learning objectives and expectations.

Step 4: Define the assignment criteria

Make a list of the knowledge and skills are you measuring with the assignment/assessment Refer to your stated learning objectives, the assignment instructions, past examples of student work, etc. for help.

  Helpful strategies for defining grading criteria:

• Collaborate with co-instructors, teaching assistants, and other colleagues
• Brainstorm and discuss with students
• Can they be observed and measured?
• Are they important and essential?
• Are they distinct from other criteria?
• Are they phrased in precise, unambiguous language?
• Revise the criteria as needed
• Consider whether some are more important than others, and how you will weight them.

Step 5: Design the rating scale

Most ratings scales include between 3 and 5 levels. Consider the following questions when designing your rating scale:

• Given what students are able to demonstrate in this assignment/assessment, what are the possible levels of achievement?
• How many levels would you like to include (more levels means more detailed descriptions)
• Will you use numbers and/or descriptive labels for each level of performance? (for example 5, 4, 3, 2, 1 and/or Exceeds expectations, Accomplished, Proficient, Developing, Beginning, etc.)
• Don’t use too many columns, and recognize that some criteria can have more columns that others . The rubric needs to be comprehensible and organized. Pick the right amount of columns so that the criteria flow logically and naturally across levels.

Step 6: Write descriptions for each level of the rating scale

Artificial Intelligence tools like Chat GPT have proven to be useful tools for creating a rubric. You will want to engineer your prompt that you provide the AI assistant to ensure you get what you want. For example, you might provide the assignment description, the criteria you feel are important, and the number of levels of performance you want in your prompt. Use the results as a starting point, and adjust the descriptions as needed.

Building a rubric from scratch

For a single-point rubric , describe what would be considered “proficient,” i.e. B-level work, and provide that description. You might also include suggestions for students outside of the actual rubric about how they might surpass proficient-level work.

For analytic and holistic rubrics , c reate statements of expected performance at each level of the rubric.

• Consider what descriptor is appropriate for each criteria, e.g., presence vs absence, complete vs incomplete, many vs none, major vs minor, consistent vs inconsistent, always vs never. If you have an indicator described in one level, it will need to be described in each level.
• You might start with the top/exemplary level. What does it look like when a student has achieved excellence for each/every criterion? Then, look at the “bottom” level. What does it look like when a student has not achieved the learning goals in any way? Then, complete the in-between levels.
• For an analytic rubric , do this for each particular criterion of the rubric so that every cell in the table is filled. These descriptions help students understand your expectations and their performance in regard to those expectations.

Well-written descriptions:

• Describe observable and measurable behavior
• Use parallel language across the scale
• Indicate the degree to which the standards are met

Step 7: Create your rubric

Create your rubric in a table or spreadsheet in Word, Google Docs, Sheets, etc., and then transfer it by typing it into Moodle. You can also use online tools to create the rubric, but you will still have to type the criteria, indicators, levels, etc., into Moodle. Rubric creators: Rubistar , iRubric

Step 8: Pilot-test your rubric

Prior to implementing your rubric on a live course, obtain feedback from:

• Teacher assistants

Try out your new rubric on a sample of student work. After you pilot-test your rubric, analyze the results to consider its effectiveness and revise accordingly.

• Limit the rubric to a single page for reading and grading ease
• Use parallel language . Use similar language and syntax/wording from column to column. Make sure that the rubric can be easily read from left to right or vice versa.
• Use student-friendly language . Make sure the language is learning-level appropriate. If you use academic language or concepts, you will need to teach those concepts.
• Share and discuss the rubric with your students . Students should understand that the rubric is there to help them learn, reflect, and self-assess. If students use a rubric, they will understand the expectations and their relevance to learning.
• Consider scalability and reusability of rubrics. Create rubric templates that you can alter as needed for multiple assignments.
• Maximize the descriptiveness of your language. Avoid words like “good” and “excellent.” For example, instead of saying, “uses excellent sources,” you might describe what makes a resource excellent so that students will know. You might also consider reducing the reliance on quantity, such as a number of allowable misspelled words. Focus instead, for example, on how distracting any spelling errors are.

Example of an analytic rubric for a final paper

Example of a holistic rubric for a final paper, single-point rubric, more examples:.

• Single Point Rubric Template ( variation )
• Analytic Rubric Template make a copy to edit
• A Rubric for Rubrics
• Bank of Online Discussion Rubrics in different formats
• Mathematical Presentations Descriptive Rubric
• Math Proof Assessment Rubric
• Kansas State Sample Rubrics
• Design Single Point Rubric

Technology Tools: Rubrics in Moodle

• Moodle Docs: Rubrics
• Moodle Docs: Grading Guide (use for single-point rubrics)

Tools with rubrics (other than Moodle)

• Google Assignments
• Turnitin Assignments: Rubric or Grading Form

Other resources

• DePaul University (n.d.). Rubrics .
• Gonzalez, J. (2014). Know your terms: Holistic, Analytic, and Single-Point Rubrics . Cult of Pedagogy.
• Goodrich, H. (1996). Understanding rubrics . Teaching for Authentic Student Performance, 54 (4), 14-17. Retrieved from   
• Miller, A. (2012). Tame the beast: tips for designing and using rubrics.
• Ragupathi, K., Lee, A. (2020). Beyond Fairness and Consistency in Grading: The Role of Rubrics in Higher Education. In: Sanger, C., Gleason, N. (eds) Diversity and Inclusion in Global Higher Education. Palgrave Macmillan, Singapore.

Examples of Rubrics

Here are some rubric examples from different colleges and universities, as well as the Association of American Colleges and Universities (AACU) VALUE rubrics. We would also like to include examples from Syracuse University faculty and staff. If you would be willing to share your rubric with us, please click  here.

• Art and Design Rubric (Rhode Island University)
• Theater Arts Writing Rubric (California State University)

Class Participation

• Holistic Participation Rubric (University of Virginia)
• Large Lecture Courses with TAs (Carnegie Mellon University)

Doctoral Program Milestones

• Qualifying Examination (Syracuse University)
• Comprehensive Core Examination (Portland State University)
• Dissertation Proposal (Portland State University)
• Dissertation (Portland State University)

Experiential Learning

• Key Competencies in Community-Engaged Learning and Teaching (Campus Compact)
• Global Learning and Intercultural Knowledge (International Cross-Cultural Experiential Learning Evaluation Toolkit)

Humanities and Social Science

• Anthropology Paper (Carnegie Mellon University)
• Economics Paper (University of Kentucky)
• History Paper (Carnegie Mellon University)
• Literary Analysis (Minnesota State University)
• Philosophy Paper (Carnegie Mellon University)
• Psychology Paper (Loyola Marymount University)
• Sociology Paper (University of California)

Media and Design

• Media and Design Elements Rubric (Samford University)

Natural Science

• Physics Paper (Illinois State University)
• Chemistry Paper (Utah State University)
• Biology Research Report (Loyola Marymount University)

Online Learning

• Discussion Forums (Simmons College)

Syracuse University’s Shared Competencies

Ethics, Integrity, and Commitment to Diversity and Inclusion rubric (*pdf)

Critical and Creative Thinking rubric (*pdf)

Scientific Inquiry and Research Skills rubric (*pdf)

Civic and Global Responsibility rubric (*pdf)

Communication Skills rubric (*pdf)

Information Literacy and Technological Agility rubric (*pdf)

• Journal Reflection (The State University of New Jersey)
• Reflection Writing Rubric  and  Research Project Writing (Carnegie Mellon University)
• Research Paper Rubric (Cornell College)
• Assessment Rubric for Student Reflections

AACU VALUE Rubrics

VALUE (Valid Assessment of Learning in Undergraduate Education) is a national assessment initiative on college student learning sponsored by AACU as part of its Liberal Education and America’s Promise (LEAP) initiative.

Intellectual and Practical Skills

• Inquiry and Analysis (*pdf)
• Critical Thinking (*pdf)
• Creative Thinking (*pdf)
• Written Communication (*pdf)
• Oral Communication (*pdf)
• Reading (*pdf)
• Quantitative Literacy (*pdf)
• Information Literacy (*pdf)
• Teamwork (*pdf)
• Problem Solving (*pdf)

Personal and Social Responsibility

• Civic Engagement (*pdf)
• Intercultural Knowledge and Competence (*pdf)
• Ethical Reasoning (*pdf)
• Foundations and Skills for Lifelong Learning (*pdf)
• Global Learning (*pdf)

Integrative and Applied Learning

• Integrative Learning (*pdf)

Assessing Institution-Wide Diversity

• Self-Assessment Rubric For the Institutionalization of Diversity, Equity, and Inclusion in Higher Education

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Amanda Hoover

Students Are Likely Writing Millions of Papers With AI

Students have submitted more than 22 million papers that may have used generative AI in the past year, new data released by plagiarism detection company Turnitin shows.

A year ago, Turnitin rolled out an AI writing detection tool that was trained on its trove of papers written by students as well as other AI-generated texts. Since then, more than 200 million papers have been reviewed by the detector, predominantly written by high school and college students. Turnitin found that 11 percent may contain AI-written language in 20 percent of its content, with 3 percent of the total papers reviewed getting flagged for having 80 percent or more AI writing. (Turnitin is owned by Advance, which also owns Condé Nast, publisher of WIRED.) Turnitin says its detector has a false positive rate of less than 1 percent when analyzing full documents.

ChatGPT’s launch was met with knee-jerk fears that the English class essay would die . The chatbot can synthesize information and distill it near-instantly—but that doesn’t mean it always gets it right. Generative AI has been known to hallucinate , creating its own facts and citing academic references that don’t actually exist. Generative AI chatbots have also been caught spitting out biased text on gender and race . Despite those flaws, students have used chatbots for research, organizing ideas, and as a ghostwriter . Traces of chatbots have even been found in peer-reviewed, published academic writing .

Teachers understandably want to hold students accountable for using generative AI without permission or disclosure. But that requires a reliable way to prove AI was used in a given assignment. Instructors have tried at times to find their own solutions to detecting AI in writing, using messy, untested methods to enforce rules , and distressing students. Further complicating the issue, some teachers are even using generative AI in their grading processes.

Detecting the use of gen AI is tricky. It’s not as easy as flagging plagiarism, because generated text is still original text. Plus, there’s nuance to how students use gen AI; some may ask chatbots to write their papers for them in large chunks or in full, while others may use the tools as an aid or a brainstorm partner.

Students also aren't tempted by only ChatGPT and similar large language models. So-called word spinners are another type of AI software that rewrites text, and may make it less obvious to a teacher that work was plagiarized or generated by AI. Turnitin’s AI detector has also been updated to detect word spinners, says Annie Chechitelli, the company’s chief product officer. It can also flag work that was rewritten by services like spell checker Grammarly, which now has its own generative AI tool . As familiar software increasingly adds generative AI components, what students can and can’t use becomes more muddled.

Detection tools themselves have a risk of bias. English language learners may be more likely to set them off; a 2023 study found a 61.3 percent false positive rate when evaluating Test of English as a Foreign Language (TOEFL) exams with seven different AI detectors. The study did not examine Turnitin’s version. The company says it has trained its detector on writing from English language learners as well as native English speakers. A study published in October found that Turnitin was among the most accurate of 16 AI language detectors in a test that had the tool examine undergraduate papers and AI-generated papers.

Amit Katwala

Andy Greenberg

Caroline Haskins

Schools that use Turnitin had access to the AI detection software for a free pilot period, which ended at the start of this year. Chechitelli says a majority of the service’s clients have opted to purchase the AI detection. But the risks of false positives and bias against English learners have led some universities to ditch the tools for now. Montclair State University in New Jersey announced in November that it would pause use of Turnitin’s AI detector. Vanderbilt University and Northwestern University did the same last summer.

“This is hard. I understand why people want a tool,” says Emily Isaacs, executive director of the Office of Faculty Excellence at Montclair State. But Isaacs says the university is concerned about potentially biased results from AI detectors, as well as the fact that the tools can’t provide confirmation the way they can with plagiarism. Plus, Montclair State doesn’t want to put a blanket ban on AI, which will have some place in academia. With time and more trust in the tools, the policies could change. “It’s not a forever decision, it’s a now decision,” Isaacs says.

Chechitelli says the Turnitin tool shouldn’t be the only consideration in passing or failing a student. Instead, it’s a chance for teachers to start conversations with students that touch on all of the nuance in using generative AI. “People don’t really know where that line should be,” she says.

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Fall 2024 CSCI Special Topics Courses

Cloud computing.

Meeting Time: 09:45 AM‑11:00 AM TTh  Instructor: Ali Anwar Course Description: Cloud computing serves many large-scale applications ranging from search engines like Google to social networking websites like Facebook to online stores like Amazon. More recently, cloud computing has emerged as an essential technology to enable emerging fields such as Artificial Intelligence (AI), the Internet of Things (IoT), and Machine Learning. The exponential growth of data availability and demands for security and speed has made the cloud computing paradigm necessary for reliable, financially economical, and scalable computation. The dynamicity and flexibility of Cloud computing have opened up many new forms of deploying applications on infrastructure that cloud service providers offer, such as renting of computation resources and serverless computing.    This course will cover the fundamentals of cloud services management and cloud software development, including but not limited to design patterns, application programming interfaces, and underlying middleware technologies. More specifically, we will cover the topics of cloud computing service models, data centers resource management, task scheduling, resource virtualization, SLAs, cloud security, software defined networks and storage, cloud storage, and programming models. We will also discuss data center design and management strategies, which enable the economic and technological benefits of cloud computing. Lastly, we will study cloud storage concepts like data distribution, durability, consistency, and redundancy. Registration Prerequisites: CS upper div, CompE upper div., EE upper div., EE grad, ITI upper div., Univ. honors student, or dept. permission; no cr for grads in CSci. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/6BvbUwEkBK41tPJ17 ).

CSCI 5980/8980 

Machine learning for healthcare: concepts and applications.

Meeting Time: 11:15 AM‑12:30 PM TTh  Instructor: Yogatheesan Varatharajah Course Description: Machine Learning is transforming healthcare. This course will introduce students to a range of healthcare problems that can be tackled using machine learning, different health data modalities, relevant machine learning paradigms, and the unique challenges presented by healthcare applications. Applications we will cover include risk stratification, disease progression modeling, precision medicine, diagnosis, prognosis, subtype discovery, and improving clinical workflows. We will also cover research topics such as explainability, causality, trust, robustness, and fairness.

Registration Prerequisites: CSCI 5521 or equivalent. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/z8X9pVZfCWMpQQ6o6  ).

Visualization with AI

Meeting Time: 04:00 PM‑05:15 PM TTh  Instructor: Qianwen Wang Course Description: This course aims to investigate how visualization techniques and AI technologies work together to enhance understanding, insights, or outcomes.

This is a seminar style course consisting of lectures, paper presentation, and interactive discussion of the selected papers. Students will also work on a group project where they propose a research idea, survey related studies, and present initial results.

This course will cover the application of visualization to better understand AI models and data, and the use of AI to improve visualization processes. Readings for the course cover papers from the top venues of AI, Visualization, and HCI, topics including AI explainability, reliability, and Human-AI collaboration.    This course is designed for PhD students, Masters students, and advanced undergraduates who want to dig into research.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/YTF5EZFUbQRJhHBYA  ). Although the class is primarily intended for PhD students, motivated juniors/seniors and MS students who are interested in this topic are welcome to apply, ensuring they detail their qualifications for the course.

Visualizations for Intelligent AR Systems

Meeting Time: 04:00 PM‑05:15 PM MW  Instructor: Zhu-Tian Chen Course Description: This course aims to explore the role of Data Visualization as a pivotal interface for enhancing human-data and human-AI interactions within Augmented Reality (AR) systems, thereby transforming a broad spectrum of activities in both professional and daily contexts. Structured as a seminar, the course consists of two main components: the theoretical and conceptual foundations delivered through lectures, paper readings, and discussions; and the hands-on experience gained through small assignments and group projects. This class is designed to be highly interactive, and AR devices will be provided to facilitate hands-on learning.    Participants will have the opportunity to experience AR systems, develop cutting-edge AR interfaces, explore AI integration, and apply human-centric design principles. The course is designed to advance students' technical skills in AR and AI, as well as their understanding of how these technologies can be leveraged to enrich human experiences across various domains. Students will be encouraged to create innovative projects with the potential for submission to research conferences.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/Y81FGaJivoqMQYtq5 ). Students are expected to have a solid foundation in either data visualization, computer graphics, computer vision, or HCI. Having expertise in all would be perfect! However, a robust interest and eagerness to delve into these subjects can be equally valuable, even though it means you need to learn some basic concepts independently.

Sustainable Computing: A Systems View

Meeting Time: 09:45 AM‑11:00 AM  Instructor: Abhishek Chandra Course Description: In recent years, there has been a dramatic increase in the pervasiveness, scale, and distribution of computing infrastructure: ranging from cloud, HPC systems, and data centers to edge computing and pervasive computing in the form of micro-data centers, mobile phones, sensors, and IoT devices embedded in the environment around us. The growing amount of computing, storage, and networking demand leads to increased energy usage, carbon emissions, and natural resource consumption. To reduce their environmental impact, there is a growing need to make computing systems sustainable. In this course, we will examine sustainable computing from a systems perspective. We will examine a number of questions:   • How can we design and build sustainable computing systems?   • How can we manage resources efficiently?   • What system software and algorithms can reduce computational needs?    Topics of interest would include:   • Sustainable system design and architectures   • Sustainability-aware systems software and management   • Sustainability in large-scale distributed computing (clouds, data centers, HPC)   • Sustainability in dispersed computing (edge, mobile computing, sensors/IoT)

Registration Prerequisites: This course is targeted towards students with a strong interest in computer systems (Operating Systems, Distributed Systems, Networking, Databases, etc.). Background in Operating Systems (Equivalent of CSCI 5103) and basic understanding of Computer Networking (Equivalent of CSCI 4211) is required.

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Harvard and Caltech Will Require Test Scores for Admission

The universities are the latest highly selective schools to end their policies that made submitting SAT or ACT scores optional.

By Anemona Hartocollis and Stephanie Saul

Harvard will reinstate standardized testing as a requirement of admission, the university announced Thursday, becoming the latest in a series of highly competitive universities to reverse their test-optional policies.

Students applying to enter Harvard in fall 2025 and beyond will be required to submit SAT or ACT scores, though the university said a few other test scores will be accepted in “exceptional cases,” including Advanced Placement or International Baccalaureate tests. The university had previously said it was going to keep its test-optional policy through the entering class of fall 2026.

Within hours of Harvard’s announcement, Caltech, a science and engineering institute, also said it was reinstating its testing requirements for students applying for admission in fall 2025.

The schools had been among nearly 2,000 colleges across the country that dropped test score requirements over the last few years, a trend that escalated during the pandemic when it was harder for students to get to test sites.

Dropping test score requirements was widely viewed as a tool to help diversify admissions, by encouraging poor and underrepresented students who had potential but did not score well on the tests to apply. But supporters of the tests have said without scores, it became harder to identify promising students who outperformed in their environments.

In explaining its decision to accelerate the return to testing, Harvard cited a study by Opportunity Insights , which found that test scores were a better predictor of academic success in college than high school grades and that they can help admissions officers identify highly talented students from low income groups who might otherwise had gone unnoticed.

“Standardized tests are a means for all students, regardless of their background and life experience, to provide information that is predictive of success in college and beyond,” Hopi Hoekstra, dean of the faculty of arts and sciences, said in a statement announcing the move.

“In short, more information, especially such strongly predictive information, is valuable for identifying talent from across the socioeconomic range,” she added.

Caltech, in Pasadena, Calif., said that reinstating testing requirements reaffirmed the school’s “commitment as a community of scientists and engineers to using all relevant data in its decision-making processes.”

Harvard and Caltech join a growing number of schools, notable for their selectivity, that have since reversed their policies, including Brown, Yale, Dartmouth, M.I.T., Georgetown, Purdue and the University of Texas at Austin.

For Harvard, the move comes at a time of transition, and perhaps a return to more conservative policies.

Last June, the Supreme Court struck down race-conscious college admissions in cases involving Harvard and the University of North Carolina, raising fears that with the demise of affirmative action, those schools would become less diverse.

And in January, Harvard’s first Black president, Claudine Gay, resigned under pressure from critics who said she had not acted strongly enough to combat antisemitism on campus after the Oct. 7 attack by Hamas on Israel, and under mounting accusations of plagiarism in her academic work, which she stood by.

The provost, Alan Garber, was named interim president, while the dean of the law school, John Manning, became interim provost, the university’s second-highest administrative position. Mr. Manning is considered a strong potential candidate to replace Dr. Gay. His background stands out for his conservative associations, having clerked for the former Supreme Court justice Antonin Scalia.

In the current climate on campus, a return to test scores could be seen as a return to tradition. It also may address concerns of many parents that the college admissions process, especially in elite institutions, is inscrutable and disconnected from merit.

Applications to Harvard were down by 5 percent this year, while those at many of its peer universities went up, suggesting that the recent turmoil may have dented its reputation. But it still received a staggering number of undergraduate applications — 54,008 — and admitted only 3.6 percent. Requiring test scores could make sorting through applications more manageable.

Critics of standardized tests have long raised concerns that the tests helped fuel inequality because some wealthier students raised their scores through high-priced tutoring. But recent studies have found that test scores help predict college grades, chances of graduation and post-college success, and that test scores are more reliable than high school grades, partly because of grade inflation in recent years .

But Robert Schaeffer, director of public education at FairTest, an organization that opposes standardized testing, said Thursday that the Opportunity Insights analysis had been criticized by other researchers. “Those scholars say that when you eliminate the role of wealth, test scores are not better than high school G.P.A.,” he said, adding that it is not clear whether that pattern is true among the admissions pool at super selective colleges such as Harvard.

Mr. Schaeffer said that at least 1,850 universities remain test optional, including Michigan, Vanderbilt, Wisconsin and Syracuse, which have recently extended their policies. “The vast majority of colleges will not require test scores.” An exception, he said, could be the University of North Carolina system, which is considering a plan to require tests, but only for those students with a G.P.A. below 2.8.

Acknowledging the concerns of critics, Harvard said that it would reassess the new policy regularly. The school said that test scores would be considered along with other information about an applicant’s experience, skills, talents, contributions to communities and references. They will also be looked at in the context of how other students are doing at the same high school.

“Admissions officers understand that not all students attend well-resourced schools, and those who come from modest economic backgrounds or first-generation college families may have had fewer opportunities to prepare for standardized tests,” William R. Fitzsimmons, Harvard’s dean of admissions and financial aid, said in a statement.

Harvard said that in the interest of selecting a diverse student body, it has enhanced financial aid and stepped up recruitment of underserved students by joining a consortium of 30 public and private universities that recruits students from rural communities.

An earlier version of this article misstated Robert Schaeffer’s position. He is the director of public education at FairTest, not the director.

How we handle corrections

Anemona Hartocollis is a national reporter for The Times, covering higher education. More about Anemona Hartocollis

Stephanie Saul reports on colleges and universities, with a recent focus on the dramatic changes in college admissions and the debate around diversity, equity and inclusion in higher education. More about Stephanie Saul

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Call for Papers 'Precarity in Urban China: Surviving in Capitalist Ruins'

17 April 2024

We are inviting research papers for 15-minute presentations as part of an in-person only workshop at the Institute of Advanced Studies on 21st June, 2024. Deadline for submissions: 15th May, 2024

Keynote speakers

Prof Margaret Hillenbrand, University of Oxford Dr Carwyn Morris, University of Leiden

The Chinese city now exists in a time and space where the economy slows, work intensifies, and Xi Jinping’s “Chinese Dream” of social mobility seems to dim. In this context, surviving and thriving in the city has become increasingly resource intensive and experiences of precarity have diversified. As Margaret Hillenbrand (2023) has recently demonstrated, states of precarity in China’s urban spaces have been largely underexplored by scholars. Yet exploring precarity in Chinese cities can help us scrutinise the “global city” (Saskia Sassen, 1991) with a local eye: international capitalism under state-managed conditions has created particular pressures and responses which call for academic investigation. 

Funded by the IAS Critical Area Studies Fund , this half-day workshop uses Anna Tsing’s (2015) The Mushroom at the End of the World as a gateway to invite participants from the humanities and social sciences to explore these local conditions, particularly in connection to the idea that meaningful lives and meaning are pieced together in the “ruins” of capitalism. The concept of capitalist ruins invokes images of what is left behind in the wake of capitalist advancement and reminds us that capitalism has boundaries and externality, domains of non-capitalist experience from which capitalism itself scavenges. Using Tsing’s work as an entry point, this workshop invites researchers to think of their work in China’s cities in connection to these notions of “salvage accumulation,” and to explore the “landscapes of unintentional design” that rapid development leaves behind, while also drawing attention to the global pull of supply chains and markets. Ranging from lived experiences of precarity and informal work, the gig economy and social media livelihoods, to urban exploration, to urban design and planning policy, through to play and rebellion in the city, this workshop aims to highlight the Chinese city as both a space of precarity and a space made out of the creative response to that precarity. 

We warmly welcome contributors from researchers at all career stages to participate in two panels of 3-4 papers and discussions. To apply, please submit the following information:

• Presentation title and 300-word abstract
• 100-word bio
• Email to the organisers, Dr Alison Lamont (IOE, UCL) and Dr Annabella Massey (IOE, UCL) at  [email protected] by 5pm BST on Wednesday 15th May, 2024

Please note this workshop will take place in-person at UCL, London. Refreshments will be provided during the day, and a speaker’s dinner will be provided after the event at a local restaurant.