How to design a system that supports AI scoring in online question answering

How to design a system that supports AI scoring in online answering questions

With the rapid development of artificial intelligence technology, the traditional manual marking method has been unable to meet the needs of large-scale The need to answer questions online. In order to improve efficiency and accuracy, it is necessary to design a system that supports AI scoring in online question answering. This article will describe how to design such a system and give specific code examples.

1. Requirements Analysis
Before designing, we must first clarify the system requirements. An AI scoring system that supports online answering needs to have the following key functions:

1. Import and display of questions: The system should support importing questions and display the interface to facilitate students to answer questions.
2. Answer submission and saving: After students complete answering questions, the submission and saving of answers should be supported.
3. Answer scoring: The system should be able to score the answers submitted by students and give accurate scores.
4. Grading result display: The system should be able to display the scoring results to students, including score status and wrong question prompts.

2. System design
Based on the above requirements, the following modules can be designed:

1. Question bank management module: used to manage the question bank, including importing questions and answers , as well as operations such as querying and modifying questions.
2. User management module: used to manage student information, including registration, login, query and modification operations.
3. Answer record management module: used to save students’ answer records, including answer submission time, score and other information.
4. AI scoring module: used to score based on the answers submitted by students, which can be implemented using machine learning algorithms or natural language processing technology.

3. Code Implementation
The following is a simple sample code based on Python to demonstrate how to design a system that supports AI scoring in online answering questions:

import pandas as pd

# 题库管理模块
class QuestionBank:
    def __init__(self):
        self.data = pd.DataFrame(columns=['question', 'answer'])

    def import_question(self, question, answer):
        self.data = self.data.append({'question': question, 'answer': answer}, ignore_index=True)

    def query_question(self, question):
        return self.data[self.data['question'] == question]

# 用户管理模块
class UserManager:
    def __init__(self):
        self.users = {}

    def register(self, username, password):
        self.users[username] = password

    def login(self, username, password):
        return self.users.get(username) == password

# 答题记录管理模块
class AnswerRecordManager:
    def __init__(self):
        self.records = pd.DataFrame(columns=['username', 'question', 'answer', 'score'])

    def submit_answer(self, username, question, answer, score):
        self.records = self.records.append({'username': username, 'question': question, 'answer': answer, 'score': score}, ignore_index=True)

    def query_score(self, username):
        return self.records[self.records['username'] == username]['score']

# AI评分模块
class AIGrading:
    def __init__(self, question_bank):
        self.question_bank = question_bank

    def grade_answer(self, question, answer):
        correct_answer = self.question_bank.query_question(question)['answer'].values[0]
        score = 0 if answer != correct_answer else 100
        return score

# 测试代码
question_bank = QuestionBank()
user_manager = UserManager()
answer_record_manager = AnswerRecordManager()
ai_grading = AIGrading(question_bank)

# 题库导入
question_bank.import_question('2+2=', '4')
question_bank.import_question('3+3=', '6')

# 用户注册与登录
user_manager.register('user1', 'password123')
user_manager.register('user2', 'password456')
print(user_manager.login('user1', 'password123'))  # True
print(user_manager.login('user1', 'wrongpassword'))  # False

# 答题记录提交与评分
answer_record_manager.submit_answer('user1', '2+2=', '4', ai_grading.grade_answer('2+2=', '4'))
answer_record_manager.submit_answer('user1', '3+3=', '7', ai_grading.grade_answer('3+3=', '7'))
print(answer_record_manager.query_score('user1'))  # [100, 0]

IV , Summary
Designing a system that supports AI scoring in online question answering requires consideration of multiple aspects such as question import, answer submission, scoring, and scoring result display. Through reasonable module division and the use of appropriate data structures and algorithms, an efficient and accurate system can be realized. The above sample code provides a simple implementation idea that can be expanded and optimized according to actual needs.

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